Search | CGShares

NVIDIA shared a link
2025-06-27 19:47:09 - Translate -

Calling on LLMs: New NVIDIA AI Blueprint Helps Automate Telco Network Configuration

Telecom companies last year spent nearly billion in capital expenditures and over trillion in operating expenditures.
These large expenses are due in part to laborious manual processes that telcos face when operating networks that require continuous optimizations.
For example, telcos must constantly tune network parameters for tasks — such as transferring calls from one network to another or distributing network traffic across multiple servers — based on the time of day, user behavior, mobility and traffic type.
These factors directly affect network performance, user experience and energy consumption.
To automate these optimization processes and save costs for telcos across the globe, NVIDIA today unveiled at GTC Paris its first AI Blueprint for telco network configuration.
At the blueprint’s core are customized large language models trained specifically on telco network data — as well as the full technical and operational architecture for turning the LLMs into an autonomous, goal-driven AI agent for telcos.
Automate Network Configuration With the AI Blueprint
NVIDIA AI Blueprints — available on build.nvidia.com — are customizable AI workflow examples. They include reference code, documentation and deployment tools that show enterprise developers how to deliver business value with NVIDIA NIM microservices.
The AI Blueprint for telco network configuration — built with BubbleRAN 5G solutions and datasets — enables developers, network engineers and telecom providers to automatically optimize the configuration of network parameters using agentic AI.
This can streamline operations, reduce costs and significantly improve service quality by embedding continuous learning and adaptability directly into network infrastructures.
Traditionally, network configurations required manual intervention or followed rigid rules to adapt to dynamic network conditions. These approaches limited adaptability and increased operational complexities, costs and inefficiencies.
The new blueprint helps shift telco operations from relying on static, rules-based systems to operations based on dynamic, AI-driven automation. It enables developers to build advanced, telco-specific AI agents that make real-time, intelligent decisions and autonomously balance trade-offs — such as network speed versus interference, or energy savings versus utilization — without human input.
Powered and Deployed by Industry Leaders
Trained on 5G data generated by BubbleRAN, and deployed on the BubbleRAN 5G O-RAN platform, the blueprint provides telcos with insight on how to set various parameters to reach performance goals, like achieving a certain bitrate while choosing an acceptable signal-to-noise ratio — a measure that impacts voice quality and thus user experience.
With the new AI Blueprint, network engineers can confidently set initial parameter values and update them as demanded by continuous network changes.
Norway-based Telenor Group, which serves over 200 million customers globally, is the first telco to integrate the AI Blueprint for telco network configuration as part of its initiative to deploy intelligent, autonomous networks that meet the performance and agility demands of 5G and beyond.
“The blueprint is helping us address configuration challenges and enhance quality of service during network installation,” said Knut Fjellheim, chief technology innovation officer at Telenor Maritime. “Implementing it is part of our push toward network automation and follows the successful deployment of agentic AI for real-time network slicing in a private 5G maritime use case.”
Industry Partners Deploy Other NVIDIA-Powered Autonomous Network Technologies
The AI Blueprint for telco network configuration is just one of many announcements at NVIDIA GTC Paris showcasing how the telecom industry is using agentic AI to make autonomous networks a reality.
Beyond the blueprint, leading telecom companies and solutions providers are tapping into NVIDIA accelerated computing, software and microservices to provide breakthrough innovations poised to vastly improve networks and communications services — accelerating the progress to autonomous networks and improving customer experiences.
NTT DATA is powering its agentic platform for telcos with NVIDIA accelerated compute and the NVIDIA AI Enterprise software platform. Its first agentic use case is focused on network alarms management, where NVIDIA NIM microservices help automate and power observability, troubleshooting, anomaly detection and resolution with closed loop ticketing.
Tata Consultancy Services is delivering agentic AI solutions for telcos built on NVIDIA DGX Cloud and using NVIDIA AI Enterprise to develop, fine-tune and integrate large telco models into AI agent workflows. These range from billing and revenue assurance, autonomous network management to hybrid edge-cloud distributed inference.
For example, the company’s anomaly management agentic AI model includes real-time detection and resolution of network anomalies and service performance optimization. This increases business agility and improves operational efficiencies by up to 40% by eliminating human intensive toils, overheads and cross-departmental silos.
Prodapt has introduced an autonomous operations workflow for networks, powered by NVIDIA AI Enterprise, that offers agentic AI capabilities to support autonomous telecom networks. AI agents can autonomously monitor networks, detect anomalies in real time, initiate diagnostics, analyze root causes of issues using historical data and correlation techniques, automatically execute corrective actions, and generate, enrich and assign incident tickets through integrated ticketing systems.
Accenture announced its new portfolio of agentic AI solutions for telecommunications through its AI Refinery platform, built on NVIDIA AI Enterprise software and accelerated computing.
The first available solution, the NOC Agentic App, boosts network operations center tasks by using a generative AI-driven, nonlinear agentic framework to automate processes such as incident and fault management, root cause analysis and configuration planning. Using the Llama 3.1 70B NVIDIA NIM microservice and the AI Refinery Distiller Framework, the NOC Agentic App orchestrates networks of intelligent agents for faster, more efficient decision-making.
Infosys is announcing its agentic autonomous operations platform, called Infosys Smart Network Assurance, designed to accelerate telecom operators’ journeys toward fully autonomous network operations.
ISNA helps address long-standing operational challenges for telcos — such as limited automation and high average time to repair — with an integrated, AI-driven platform that reduces operational costs by up to 40% and shortens fault resolution times by up to 30%. NVIDIA NIM and NeMo microservices enhance the platform’s reasoning and hallucination-detection capabilities, reduce latency and increase accuracy.
Get started with the new blueprint today.
Learn more about the latest AI advancements for telecom and other industries at NVIDIA GTC Paris, running through Thursday, June 12, at VivaTech, including a keynote from NVIDIA founder and CEO Jensen Huang and a special address from Ronnie Vasishta, senior vice president of telecom at NVIDIA. Plus, hear from industry leaders in a panel session with Orange, Swisscom, Telenor and NVIDIA.
#calling #llms #new #nvidia #blueprint

Calling on LLMs: New NVIDIA AI Blueprint Helps Automate Telco Network Configuration
Telecom companies last year spent nearly billion in capital expenditures and over trillion in operating expenditures. These large expenses are due in part to laborious manual processes that telcos face when operating networks that require continuous optimizations. For example, telcos must constantly tune network parameters for tasks — such as transferring calls from one network to another or distributing network traffic across multiple servers — based on the time of day, user behavior, mobility and traffic type. These factors directly affect network performance, user experience and energy consumption. To automate these optimization processes and save costs for telcos across the globe, NVIDIA today unveiled at GTC Paris its first AI Blueprint for telco network configuration. At the blueprint’s core are customized large language models trained specifically on telco network data — as well as the full technical and operational architecture for turning the LLMs into an autonomous, goal-driven AI agent for telcos. Automate Network Configuration With the AI Blueprint NVIDIA AI Blueprints — available on build.nvidia.com — are customizable AI workflow examples. They include reference code, documentation and deployment tools that show enterprise developers how to deliver business value with NVIDIA NIM microservices. The AI Blueprint for telco network configuration — built with BubbleRAN 5G solutions and datasets — enables developers, network engineers and telecom providers to automatically optimize the configuration of network parameters using agentic AI. This can streamline operations, reduce costs and significantly improve service quality by embedding continuous learning and adaptability directly into network infrastructures. Traditionally, network configurations required manual intervention or followed rigid rules to adapt to dynamic network conditions. These approaches limited adaptability and increased operational complexities, costs and inefficiencies. The new blueprint helps shift telco operations from relying on static, rules-based systems to operations based on dynamic, AI-driven automation. It enables developers to build advanced, telco-specific AI agents that make real-time, intelligent decisions and autonomously balance trade-offs — such as network speed versus interference, or energy savings versus utilization — without human input. Powered and Deployed by Industry Leaders Trained on 5G data generated by BubbleRAN, and deployed on the BubbleRAN 5G O-RAN platform, the blueprint provides telcos with insight on how to set various parameters to reach performance goals, like achieving a certain bitrate while choosing an acceptable signal-to-noise ratio — a measure that impacts voice quality and thus user experience. With the new AI Blueprint, network engineers can confidently set initial parameter values and update them as demanded by continuous network changes. Norway-based Telenor Group, which serves over 200 million customers globally, is the first telco to integrate the AI Blueprint for telco network configuration as part of its initiative to deploy intelligent, autonomous networks that meet the performance and agility demands of 5G and beyond. “The blueprint is helping us address configuration challenges and enhance quality of service during network installation,” said Knut Fjellheim, chief technology innovation officer at Telenor Maritime. “Implementing it is part of our push toward network automation and follows the successful deployment of agentic AI for real-time network slicing in a private 5G maritime use case.” Industry Partners Deploy Other NVIDIA-Powered Autonomous Network Technologies The AI Blueprint for telco network configuration is just one of many announcements at NVIDIA GTC Paris showcasing how the telecom industry is using agentic AI to make autonomous networks a reality. Beyond the blueprint, leading telecom companies and solutions providers are tapping into NVIDIA accelerated computing, software and microservices to provide breakthrough innovations poised to vastly improve networks and communications services — accelerating the progress to autonomous networks and improving customer experiences. NTT DATA is powering its agentic platform for telcos with NVIDIA accelerated compute and the NVIDIA AI Enterprise software platform. Its first agentic use case is focused on network alarms management, where NVIDIA NIM microservices help automate and power observability, troubleshooting, anomaly detection and resolution with closed loop ticketing. Tata Consultancy Services is delivering agentic AI solutions for telcos built on NVIDIA DGX Cloud and using NVIDIA AI Enterprise to develop, fine-tune and integrate large telco models into AI agent workflows. These range from billing and revenue assurance, autonomous network management to hybrid edge-cloud distributed inference. For example, the company’s anomaly management agentic AI model includes real-time detection and resolution of network anomalies and service performance optimization. This increases business agility and improves operational efficiencies by up to 40% by eliminating human intensive toils, overheads and cross-departmental silos. Prodapt has introduced an autonomous operations workflow for networks, powered by NVIDIA AI Enterprise, that offers agentic AI capabilities to support autonomous telecom networks. AI agents can autonomously monitor networks, detect anomalies in real time, initiate diagnostics, analyze root causes of issues using historical data and correlation techniques, automatically execute corrective actions, and generate, enrich and assign incident tickets through integrated ticketing systems. Accenture announced its new portfolio of agentic AI solutions for telecommunications through its AI Refinery platform, built on NVIDIA AI Enterprise software and accelerated computing. The first available solution, the NOC Agentic App, boosts network operations center tasks by using a generative AI-driven, nonlinear agentic framework to automate processes such as incident and fault management, root cause analysis and configuration planning. Using the Llama 3.1 70B NVIDIA NIM microservice and the AI Refinery Distiller Framework, the NOC Agentic App orchestrates networks of intelligent agents for faster, more efficient decision-making. Infosys is announcing its agentic autonomous operations platform, called Infosys Smart Network Assurance, designed to accelerate telecom operators’ journeys toward fully autonomous network operations. ISNA helps address long-standing operational challenges for telcos — such as limited automation and high average time to repair — with an integrated, AI-driven platform that reduces operational costs by up to 40% and shortens fault resolution times by up to 30%. NVIDIA NIM and NeMo microservices enhance the platform’s reasoning and hallucination-detection capabilities, reduce latency and increase accuracy. Get started with the new blueprint today. Learn more about the latest AI advancements for telecom and other industries at NVIDIA GTC Paris, running through Thursday, June 12, at VivaTech, including a keynote from NVIDIA founder and CEO Jensen Huang and a special address from Ronnie Vasishta, senior vice president of telecom at NVIDIA. Plus, hear from industry leaders in a panel session with Orange, Swisscom, Telenor and NVIDIA. #calling #llms #new #nvidia #blueprint

BLOGS.NVIDIA.COM

Calling on LLMs: New NVIDIA AI Blueprint Helps Automate Telco Network Configuration
Telecom companies last year spent nearly $295 billion in capital expenditures and over $1 trillion in operating expenditures. These large expenses are due in part to laborious manual processes that telcos face when operating networks that require continuous optimizations. For example, telcos must constantly tune network parameters for tasks — such as transferring calls from one network to another or distributing network traffic across multiple servers — based on the time of day, user behavior, mobility and traffic type. These factors directly affect network performance, user experience and energy consumption. To automate these optimization processes and save costs for telcos across the globe, NVIDIA today unveiled at GTC Paris its first AI Blueprint for telco network configuration. At the blueprint’s core are customized large language models trained specifically on telco network data — as well as the full technical and operational architecture for turning the LLMs into an autonomous, goal-driven AI agent for telcos. Automate Network Configuration With the AI Blueprint NVIDIA AI Blueprints — available on build.nvidia.com — are customizable AI workflow examples. They include reference code, documentation and deployment tools that show enterprise developers how to deliver business value with NVIDIA NIM microservices. The AI Blueprint for telco network configuration — built with BubbleRAN 5G solutions and datasets — enables developers, network engineers and telecom providers to automatically optimize the configuration of network parameters using agentic AI. This can streamline operations, reduce costs and significantly improve service quality by embedding continuous learning and adaptability directly into network infrastructures. Traditionally, network configurations required manual intervention or followed rigid rules to adapt to dynamic network conditions. These approaches limited adaptability and increased operational complexities, costs and inefficiencies. The new blueprint helps shift telco operations from relying on static, rules-based systems to operations based on dynamic, AI-driven automation. It enables developers to build advanced, telco-specific AI agents that make real-time, intelligent decisions and autonomously balance trade-offs — such as network speed versus interference, or energy savings versus utilization — without human input. Powered and Deployed by Industry Leaders Trained on 5G data generated by BubbleRAN, and deployed on the BubbleRAN 5G O-RAN platform, the blueprint provides telcos with insight on how to set various parameters to reach performance goals, like achieving a certain bitrate while choosing an acceptable signal-to-noise ratio — a measure that impacts voice quality and thus user experience. With the new AI Blueprint, network engineers can confidently set initial parameter values and update them as demanded by continuous network changes. Norway-based Telenor Group, which serves over 200 million customers globally, is the first telco to integrate the AI Blueprint for telco network configuration as part of its initiative to deploy intelligent, autonomous networks that meet the performance and agility demands of 5G and beyond. “The blueprint is helping us address configuration challenges and enhance quality of service during network installation,” said Knut Fjellheim, chief technology innovation officer at Telenor Maritime. “Implementing it is part of our push toward network automation and follows the successful deployment of agentic AI for real-time network slicing in a private 5G maritime use case.” Industry Partners Deploy Other NVIDIA-Powered Autonomous Network Technologies The AI Blueprint for telco network configuration is just one of many announcements at NVIDIA GTC Paris showcasing how the telecom industry is using agentic AI to make autonomous networks a reality. Beyond the blueprint, leading telecom companies and solutions providers are tapping into NVIDIA accelerated computing, software and microservices to provide breakthrough innovations poised to vastly improve networks and communications services — accelerating the progress to autonomous networks and improving customer experiences. NTT DATA is powering its agentic platform for telcos with NVIDIA accelerated compute and the NVIDIA AI Enterprise software platform. Its first agentic use case is focused on network alarms management, where NVIDIA NIM microservices help automate and power observability, troubleshooting, anomaly detection and resolution with closed loop ticketing. Tata Consultancy Services is delivering agentic AI solutions for telcos built on NVIDIA DGX Cloud and using NVIDIA AI Enterprise to develop, fine-tune and integrate large telco models into AI agent workflows. These range from billing and revenue assurance, autonomous network management to hybrid edge-cloud distributed inference. For example, the company’s anomaly management agentic AI model includes real-time detection and resolution of network anomalies and service performance optimization. This increases business agility and improves operational efficiencies by up to 40% by eliminating human intensive toils, overheads and cross-departmental silos. Prodapt has introduced an autonomous operations workflow for networks, powered by NVIDIA AI Enterprise, that offers agentic AI capabilities to support autonomous telecom networks. AI agents can autonomously monitor networks, detect anomalies in real time, initiate diagnostics, analyze root causes of issues using historical data and correlation techniques, automatically execute corrective actions, and generate, enrich and assign incident tickets through integrated ticketing systems. Accenture announced its new portfolio of agentic AI solutions for telecommunications through its AI Refinery platform, built on NVIDIA AI Enterprise software and accelerated computing. The first available solution, the NOC Agentic App, boosts network operations center tasks by using a generative AI-driven, nonlinear agentic framework to automate processes such as incident and fault management, root cause analysis and configuration planning. Using the Llama 3.1 70B NVIDIA NIM microservice and the AI Refinery Distiller Framework, the NOC Agentic App orchestrates networks of intelligent agents for faster, more efficient decision-making. Infosys is announcing its agentic autonomous operations platform, called Infosys Smart Network Assurance (ISNA), designed to accelerate telecom operators’ journeys toward fully autonomous network operations. ISNA helps address long-standing operational challenges for telcos — such as limited automation and high average time to repair — with an integrated, AI-driven platform that reduces operational costs by up to 40% and shortens fault resolution times by up to 30%. NVIDIA NIM and NeMo microservices enhance the platform’s reasoning and hallucination-detection capabilities, reduce latency and increase accuracy. Get started with the new blueprint today. Learn more about the latest AI advancements for telecom and other industries at NVIDIA GTC Paris, running through Thursday, June 12, at VivaTech, including a keynote from NVIDIA founder and CEO Jensen Huang and a special address from Ronnie Vasishta, senior vice president of telecom at NVIDIA. Plus, hear from industry leaders in a panel session with Orange, Swisscom, Telenor and NVIDIA.

80

0 Comments 0 Shares

Please log in to like, share and comment!
NVIDIA shared a link
2025-06-27 15:46:25 - Translate -

Plug and Play: Build a G-Assist Plug-In Today

Project G-Assist — available through the NVIDIA App — is an experimental AI assistant that helps tune, control and optimize NVIDIA GeForce RTX systems.
NVIDIA’s Plug and Play: Project G-Assist Plug-In Hackathon — running virtually through Wednesday, July 16 — invites the community to explore AI and build custom G-Assist plug-ins for a chance to win prizes and be featured on NVIDIA social media channels.

G-Assist allows users to control their RTX GPU and other system settings using natural language, thanks to a small language model that runs on device. It can be used from the NVIDIA Overlay in the NVIDIA App without needing to tab out or switch programs. Users can expand its capabilities via plug-ins and even connect it to agentic frameworks such as Langflow.
Below, find popular G-Assist plug-ins, hackathon details and tips to get started.
Plug-In and Win
Join the hackathon by registering and checking out the curated technical resources.
G-Assist plug-ins can be built in several ways, including with Python for rapid development, with C++ for performance-critical apps and with custom system interactions for hardware and operating system automation.
For those that prefer vibe coding, the G-Assist Plug-In Builder — a ChatGPT-based app that allows no-code or low-code development with natural language commands — makes it easy for enthusiasts to start creating plug-ins.
To submit an entry, participants must provide a GitHub repository, including source code file, requirements.txt, manifest.json, config.json, a plug-in executable file and READme code.
Then, submit a video — between 30 seconds and two minutes — showcasing the plug-in in action.
Finally, hackathoners must promote their plug-in using #AIonRTXHackathon on a social media channel: Instagram, TikTok or X. Submit projects via this form by Wednesday, July 16.
Judges will assess plug-ins based on three main criteria: 1) innovation and creativity, 2) technical execution and integration, reviewing technical depth, G-Assist integration and scalability, and 3) usability and community impact, aka how easy it is to use the plug-in.
Winners will be selected on Wednesday, Aug. 20. First place will receive a GeForce RTX 5090 laptop, second place a GeForce RTX 5080 GPU and third a GeForce RTX 5070 GPU. These top three will also be featured on NVIDIA’s social media channels, get the opportunity to meet the NVIDIA G-Assist team and earn an NVIDIA Deep Learning Institute self-paced course credit.
Project G-Assist requires a GeForce RTX 50, 40 or 30 Series Desktop GPU with at least 12GB of VRAM, Windows 11 or 10 operating system, a compatible CPU, specific disk space requirements and a recent GeForce Game Ready Driver or NVIDIA Studio Driver.
Plug-InExplore open-source plug-in samples available on GitHub, which showcase the diverse ways on-device AI can enhance PC and gaming workflows.

Popular plug-ins include:

Google Gemini: Enables search-based queries using Google Search integration and large language model-based queries using Gemini capabilities in real time without needing to switch programs from the convenience of the NVIDIA App Overlay.
Discord: Enables users to easily share game highlights or messages directly to Discord servers without disrupting gameplay.
IFTTT: Lets users create automations across hundreds of compatible endpoints to trigger IoT routines — such as adjusting room lights and smart shades, or pushing the latest gaming news to a mobile device.
Spotify: Lets users control Spotify using simple voice commands or the G-Assist interface to play favorite tracks and manage playlists.
Twitch: Checks if any Twitch streamer is currently live and can access detailed stream information such as titles, games, view counts and more.

Get G-Assist
Join the NVIDIA Developer Discord channel to collaborate, share creations and gain support from fellow AI enthusiasts and NVIDIA staff.
the date for NVIDIA’s How to Build a G-Assist Plug-In webinar on Wednesday, July 9, from 10-11 a.m. PT, to learn more about Project G-Assist capabilities, discover the fundamentals of building, testing and deploying Project G-Assist plug-ins, and participate in a live Q&A session.
Explore NVIDIA’s GitHub repository, which provides everything needed to get started developing with G-Assist, including sample plug-ins, step-by-step instructions and documentation for building custom functionalities.
Learn more about the ChatGPT Plug-In Builder to transform ideas into functional G-Assist plug-ins with minimal coding. The tool uses OpenAI’s custom GPT builder to generate plug-in code and streamline the development process.
NVIDIA’s technical blog walks through the architecture of a G-Assist plug-in, using a Twitch integration as an example. Discover how plug-ins work, how they communicate with G-Assist and how to build them from scratch.
Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about NVIDIA NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, digital humans, productivity apps and more on AI PCs and workstations.
Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter.
Follow NVIDIA Workstation on LinkedIn and X.
See notice regarding software product information.
#plug #play #build #gassist #plugin

Plug and Play: Build a G-Assist Plug-In Today
Project G-Assist — available through the NVIDIA App — is an experimental AI assistant that helps tune, control and optimize NVIDIA GeForce RTX systems. NVIDIA’s Plug and Play: Project G-Assist Plug-In Hackathon — running virtually through Wednesday, July 16 — invites the community to explore AI and build custom G-Assist plug-ins for a chance to win prizes and be featured on NVIDIA social media channels. G-Assist allows users to control their RTX GPU and other system settings using natural language, thanks to a small language model that runs on device. It can be used from the NVIDIA Overlay in the NVIDIA App without needing to tab out or switch programs. Users can expand its capabilities via plug-ins and even connect it to agentic frameworks such as Langflow. Below, find popular G-Assist plug-ins, hackathon details and tips to get started. Plug-In and Win Join the hackathon by registering and checking out the curated technical resources. G-Assist plug-ins can be built in several ways, including with Python for rapid development, with C++ for performance-critical apps and with custom system interactions for hardware and operating system automation. For those that prefer vibe coding, the G-Assist Plug-In Builder — a ChatGPT-based app that allows no-code or low-code development with natural language commands — makes it easy for enthusiasts to start creating plug-ins. To submit an entry, participants must provide a GitHub repository, including source code file, requirements.txt, manifest.json, config.json, a plug-in executable file and READme code. Then, submit a video — between 30 seconds and two minutes — showcasing the plug-in in action. Finally, hackathoners must promote their plug-in using #AIonRTXHackathon on a social media channel: Instagram, TikTok or X. Submit projects via this form by Wednesday, July 16. Judges will assess plug-ins based on three main criteria: 1) innovation and creativity, 2) technical execution and integration, reviewing technical depth, G-Assist integration and scalability, and 3) usability and community impact, aka how easy it is to use the plug-in. Winners will be selected on Wednesday, Aug. 20. First place will receive a GeForce RTX 5090 laptop, second place a GeForce RTX 5080 GPU and third a GeForce RTX 5070 GPU. These top three will also be featured on NVIDIA’s social media channels, get the opportunity to meet the NVIDIA G-Assist team and earn an NVIDIA Deep Learning Institute self-paced course credit. Project G-Assist requires a GeForce RTX 50, 40 or 30 Series Desktop GPU with at least 12GB of VRAM, Windows 11 or 10 operating system, a compatible CPU, specific disk space requirements and a recent GeForce Game Ready Driver or NVIDIA Studio Driver. Plug-InExplore open-source plug-in samples available on GitHub, which showcase the diverse ways on-device AI can enhance PC and gaming workflows. Popular plug-ins include: Google Gemini: Enables search-based queries using Google Search integration and large language model-based queries using Gemini capabilities in real time without needing to switch programs from the convenience of the NVIDIA App Overlay. Discord: Enables users to easily share game highlights or messages directly to Discord servers without disrupting gameplay. IFTTT: Lets users create automations across hundreds of compatible endpoints to trigger IoT routines — such as adjusting room lights and smart shades, or pushing the latest gaming news to a mobile device. Spotify: Lets users control Spotify using simple voice commands or the G-Assist interface to play favorite tracks and manage playlists. Twitch: Checks if any Twitch streamer is currently live and can access detailed stream information such as titles, games, view counts and more. Get G-Assist Join the NVIDIA Developer Discord channel to collaborate, share creations and gain support from fellow AI enthusiasts and NVIDIA staff. the date for NVIDIA’s How to Build a G-Assist Plug-In webinar on Wednesday, July 9, from 10-11 a.m. PT, to learn more about Project G-Assist capabilities, discover the fundamentals of building, testing and deploying Project G-Assist plug-ins, and participate in a live Q&A session. Explore NVIDIA’s GitHub repository, which provides everything needed to get started developing with G-Assist, including sample plug-ins, step-by-step instructions and documentation for building custom functionalities. Learn more about the ChatGPT Plug-In Builder to transform ideas into functional G-Assist plug-ins with minimal coding. The tool uses OpenAI’s custom GPT builder to generate plug-in code and streamline the development process. NVIDIA’s technical blog walks through the architecture of a G-Assist plug-in, using a Twitch integration as an example. Discover how plug-ins work, how they communicate with G-Assist and how to build them from scratch. Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about NVIDIA NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, digital humans, productivity apps and more on AI PCs and workstations. Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter. Follow NVIDIA Workstation on LinkedIn and X. See notice regarding software product information. #plug #play #build #gassist #plugin

BLOGS.NVIDIA.COM

Plug and Play: Build a G-Assist Plug-In Today
Project G-Assist — available through the NVIDIA App — is an experimental AI assistant that helps tune, control and optimize NVIDIA GeForce RTX systems. NVIDIA’s Plug and Play: Project G-Assist Plug-In Hackathon — running virtually through Wednesday, July 16 — invites the community to explore AI and build custom G-Assist plug-ins for a chance to win prizes and be featured on NVIDIA social media channels. G-Assist allows users to control their RTX GPU and other system settings using natural language, thanks to a small language model that runs on device. It can be used from the NVIDIA Overlay in the NVIDIA App without needing to tab out or switch programs. Users can expand its capabilities via plug-ins and even connect it to agentic frameworks such as Langflow. Below, find popular G-Assist plug-ins, hackathon details and tips to get started. Plug-In and Win Join the hackathon by registering and checking out the curated technical resources. G-Assist plug-ins can be built in several ways, including with Python for rapid development, with C++ for performance-critical apps and with custom system interactions for hardware and operating system automation. For those that prefer vibe coding, the G-Assist Plug-In Builder — a ChatGPT-based app that allows no-code or low-code development with natural language commands — makes it easy for enthusiasts to start creating plug-ins. To submit an entry, participants must provide a GitHub repository, including source code file (plugin.py), requirements.txt, manifest.json, config.json (if applicable), a plug-in executable file and READme code. Then, submit a video — between 30 seconds and two minutes — showcasing the plug-in in action. Finally, hackathoners must promote their plug-in using #AIonRTXHackathon on a social media channel: Instagram, TikTok or X. Submit projects via this form by Wednesday, July 16. Judges will assess plug-ins based on three main criteria: 1) innovation and creativity, 2) technical execution and integration, reviewing technical depth, G-Assist integration and scalability, and 3) usability and community impact, aka how easy it is to use the plug-in. Winners will be selected on Wednesday, Aug. 20. First place will receive a GeForce RTX 5090 laptop, second place a GeForce RTX 5080 GPU and third a GeForce RTX 5070 GPU. These top three will also be featured on NVIDIA’s social media channels, get the opportunity to meet the NVIDIA G-Assist team and earn an NVIDIA Deep Learning Institute self-paced course credit. Project G-Assist requires a GeForce RTX 50, 40 or 30 Series Desktop GPU with at least 12GB of VRAM, Windows 11 or 10 operating system, a compatible CPU (Intel Pentium G Series, Core i3, i5, i7 or higher; AMD FX, Ryzen 3, 5, 7, 9, Threadripper or higher), specific disk space requirements and a recent GeForce Game Ready Driver or NVIDIA Studio Driver. Plug-In(spiration) Explore open-source plug-in samples available on GitHub, which showcase the diverse ways on-device AI can enhance PC and gaming workflows. Popular plug-ins include: Google Gemini: Enables search-based queries using Google Search integration and large language model-based queries using Gemini capabilities in real time without needing to switch programs from the convenience of the NVIDIA App Overlay. Discord: Enables users to easily share game highlights or messages directly to Discord servers without disrupting gameplay. IFTTT: Lets users create automations across hundreds of compatible endpoints to trigger IoT routines — such as adjusting room lights and smart shades, or pushing the latest gaming news to a mobile device. Spotify: Lets users control Spotify using simple voice commands or the G-Assist interface to play favorite tracks and manage playlists. Twitch: Checks if any Twitch streamer is currently live and can access detailed stream information such as titles, games, view counts and more. Get G-Assist(ance) Join the NVIDIA Developer Discord channel to collaborate, share creations and gain support from fellow AI enthusiasts and NVIDIA staff. Save the date for NVIDIA’s How to Build a G-Assist Plug-In webinar on Wednesday, July 9, from 10-11 a.m. PT, to learn more about Project G-Assist capabilities, discover the fundamentals of building, testing and deploying Project G-Assist plug-ins, and participate in a live Q&A session. Explore NVIDIA’s GitHub repository, which provides everything needed to get started developing with G-Assist, including sample plug-ins, step-by-step instructions and documentation for building custom functionalities. Learn more about the ChatGPT Plug-In Builder to transform ideas into functional G-Assist plug-ins with minimal coding. The tool uses OpenAI’s custom GPT builder to generate plug-in code and streamline the development process. NVIDIA’s technical blog walks through the architecture of a G-Assist plug-in, using a Twitch integration as an example. Discover how plug-ins work, how they communicate with G-Assist and how to build them from scratch. Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about NVIDIA NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, digital humans, productivity apps and more on AI PCs and workstations. Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter. Follow NVIDIA Workstation on LinkedIn and X. See notice regarding software product information.

25

0 Comments 0 Shares

Please log in to like, share and comment!
NVIDIA shared a link
2025-06-27 13:46:30 - Translate -

NVIDIA and Partners Highlight Next-Generation Robotics, Automation and AI Technologies at Automatica

From the heart of Germany’s automotive sector to manufacturing hubs across France and Italy, Europe is embracing industrial AI and advanced AI-powered robotics to address labor shortages, boost productivity and fuel sustainable economic growth.
Robotics companies are developing humanoid robots and collaborative systems that integrate AI into real-world manufacturing applications. Supported by a billion investment initiative and coordinated efforts from the European Commission, Europe is positioning itself at the forefront of the next wave of industrial automation, powered by AI.
This momentum is on full display at Automatica — Europe’s premier conference on advancements in robotics, machine vision and intelligent manufacturing — taking place this week in Munich, Germany.
NVIDIA and its ecosystem of partners and customers are showcasing next-generation robots, automation and AI technologies designed to accelerate the continent’s leadership in smart manufacturing and logistics.
NVIDIA Technologies Boost Robotics Development
Central to advancing robotics development is Europe’s first industrial AI cloud, announced at NVIDIA GTC Paris at VivaTech earlier this month. The Germany-based AI factory, featuring 10,000 NVIDIA GPUs, provides European manufacturers with secure, sovereign and centralized AI infrastructure for industrial workloads. It will support applications ranging from design and engineering to factory digital twins and robotics.
To help accelerate humanoid development, NVIDIA released NVIDIA Isaac GR00T N1.5 — an open foundation model for humanoid robot reasoning and skills. This update enhances the model’s adaptability and ability to follow instructions, significantly improving its performance in material handling and manufacturing tasks.
To help post-train GR00T N1.5, NVIDIA has also released the Isaac GR00T-Dreams blueprint — a reference workflow for generating vast amounts of synthetic trajectory data from a small number of human demonstrations — enabling robots to generalize across behaviors and adapt to new environments with minimal human demonstration data.
In addition, early developer previews of NVIDIA Isaac Sim 5.0 and Isaac Lab 2.2 — open-source robot simulation and learning frameworks optimized for NVIDIA RTX PRO 6000 workstations — are now available on GitHub.
Image courtesy of Wandelbots.
Robotics Leaders Tap NVIDIA Simulation Technology to Develop and Deploy Humanoids and More
Robotics developers and solutions providers across the globe are integrating NVIDIA’s three computers to train, simulate and deploy robots.
NEURA Robotics, a German robotics company and pioneer for cognitive robots, unveiled the third generation of its humanoid, 4NE1, designed to assist humans in domestic and professional environments through advanced cognitive capabilities and humanlike interaction. 4NE1 is powered by GR00T N1 and was trained in Isaac Sim and Isaac Lab before real-world deployment.
NEURA Robotics is also presenting Neuraverse, a digital twin and interconnected ecosystem for robot training, skills and applications, fully compatible with NVIDIA Omniverse technologies.
Delta Electronics, a global leader in power management and smart green solutions, is debuting two next-generation collaborative robots: D-Bot Mar and D-Bot 2 in 1 — both trained using Omniverse and Isaac Sim technologies and libraries. These cobots are engineered to transform intralogistics and optimize production flows.
Wandelbots, the creator of the Wandelbots NOVA software platform for industrial robotics, is partnering with SoftServe, a global IT consulting and digital services provider, to scale simulation-first automating using NVIDIA Isaac Sim, enabling virtual validation and real-world deployment with maximum impact.
Cyngn, a pioneer in autonomous mobile robotics, is integrating its DriveMod technology into Isaac Sim to enable large-scale, high fidelity virtual testing of advanced autonomous operation. Purpose-built for industrial applications, DriveMod is already deployed on vehicles such as the Motrec MT-160 Tugger and BYD Forklift, delivering sophisticated automation to material handling operations.
Doosan Robotics, a company specializing in AI robotic solutions, will showcase its “sim to real” solution, using NVIDIA Isaac Sim and cuRobo. Doosan will be showcasing how to seamlessly transfer tasks from simulation to real robots across a wide range of applications — from manufacturing to service industries.
Franka Robotics has integrated Isaac GR00T N1.5 into a dual-arm Franka Research 3robot for robotic control. The integration of GR00T N1.5 allows the system to interpret visual input, understand task context and autonomously perform complex manipulation — without the need for task-specific programming or hardcoded logic.
Image courtesy of Franka Robotics.
Hexagon, the global leader in measurement technologies, launched its new humanoid, dubbed AEON. With its unique locomotion system and multimodal sensor fusion, and powered by NVIDIA’s three-computer solution, AEON is engineered to perform a wide range of industrial applications, from manipulation and asset inspection to reality capture and operator support.
Intrinsic, a software and AI robotics company, is integrating Intrinsic Flowstate with Omniverse and OpenUSD for advanced visualization and digital twins that can be used in many industrial use cases. The company is also using NVIDIA foundation models to enhance robot capabilities like grasp planning through AI and simulation technologies.
SCHUNK, a global leader in gripping systems and automation technology, is showcasing its innovative grasping kit powered by the NVIDIA Jetson AGX Orin module. The kit intelligently detects objects and calculates optimal grasping points. Schunk is also demonstrating seamless simulation-to-reality transfer using IGS Virtuous software — built on Omniverse technologies — to control a real robot through simulation in a pick-and-place scenario.
Universal Robots is showcasing UR15, its fastest cobot yet. Powered by the UR AI Accelerator — developed with NVIDIA and running on Jetson AGX Orin using CUDA-accelerated Isaac libraries — UR15 helps set a new standard for industrial automation.

Vention, a full-stack software and hardware automation company, launched its Machine Motion AI, built on CUDA-accelerated Isaac libraries and powered by Jetson. Vention is also expanding its lineup of robotic offerings by adding the FR3 robot from Franka Robotics to its ecosystem, enhancing its solutions for academic and research applications.
Image courtesy of Vention.
Learn more about the latest robotics advancements by joining NVIDIA at Automatica, running through Friday, June 27.
#nvidia #partners #highlight #nextgeneration #robotics

NVIDIA and Partners Highlight Next-Generation Robotics, Automation and AI Technologies at Automatica
From the heart of Germany’s automotive sector to manufacturing hubs across France and Italy, Europe is embracing industrial AI and advanced AI-powered robotics to address labor shortages, boost productivity and fuel sustainable economic growth. Robotics companies are developing humanoid robots and collaborative systems that integrate AI into real-world manufacturing applications. Supported by a billion investment initiative and coordinated efforts from the European Commission, Europe is positioning itself at the forefront of the next wave of industrial automation, powered by AI. This momentum is on full display at Automatica — Europe’s premier conference on advancements in robotics, machine vision and intelligent manufacturing — taking place this week in Munich, Germany. NVIDIA and its ecosystem of partners and customers are showcasing next-generation robots, automation and AI technologies designed to accelerate the continent’s leadership in smart manufacturing and logistics. NVIDIA Technologies Boost Robotics Development Central to advancing robotics development is Europe’s first industrial AI cloud, announced at NVIDIA GTC Paris at VivaTech earlier this month. The Germany-based AI factory, featuring 10,000 NVIDIA GPUs, provides European manufacturers with secure, sovereign and centralized AI infrastructure for industrial workloads. It will support applications ranging from design and engineering to factory digital twins and robotics. To help accelerate humanoid development, NVIDIA released NVIDIA Isaac GR00T N1.5 — an open foundation model for humanoid robot reasoning and skills. This update enhances the model’s adaptability and ability to follow instructions, significantly improving its performance in material handling and manufacturing tasks. To help post-train GR00T N1.5, NVIDIA has also released the Isaac GR00T-Dreams blueprint — a reference workflow for generating vast amounts of synthetic trajectory data from a small number of human demonstrations — enabling robots to generalize across behaviors and adapt to new environments with minimal human demonstration data. In addition, early developer previews of NVIDIA Isaac Sim 5.0 and Isaac Lab 2.2 — open-source robot simulation and learning frameworks optimized for NVIDIA RTX PRO 6000 workstations — are now available on GitHub. Image courtesy of Wandelbots. Robotics Leaders Tap NVIDIA Simulation Technology to Develop and Deploy Humanoids and More Robotics developers and solutions providers across the globe are integrating NVIDIA’s three computers to train, simulate and deploy robots. NEURA Robotics, a German robotics company and pioneer for cognitive robots, unveiled the third generation of its humanoid, 4NE1, designed to assist humans in domestic and professional environments through advanced cognitive capabilities and humanlike interaction. 4NE1 is powered by GR00T N1 and was trained in Isaac Sim and Isaac Lab before real-world deployment. NEURA Robotics is also presenting Neuraverse, a digital twin and interconnected ecosystem for robot training, skills and applications, fully compatible with NVIDIA Omniverse technologies. Delta Electronics, a global leader in power management and smart green solutions, is debuting two next-generation collaborative robots: D-Bot Mar and D-Bot 2 in 1 — both trained using Omniverse and Isaac Sim technologies and libraries. These cobots are engineered to transform intralogistics and optimize production flows. Wandelbots, the creator of the Wandelbots NOVA software platform for industrial robotics, is partnering with SoftServe, a global IT consulting and digital services provider, to scale simulation-first automating using NVIDIA Isaac Sim, enabling virtual validation and real-world deployment with maximum impact. Cyngn, a pioneer in autonomous mobile robotics, is integrating its DriveMod technology into Isaac Sim to enable large-scale, high fidelity virtual testing of advanced autonomous operation. Purpose-built for industrial applications, DriveMod is already deployed on vehicles such as the Motrec MT-160 Tugger and BYD Forklift, delivering sophisticated automation to material handling operations. Doosan Robotics, a company specializing in AI robotic solutions, will showcase its “sim to real” solution, using NVIDIA Isaac Sim and cuRobo. Doosan will be showcasing how to seamlessly transfer tasks from simulation to real robots across a wide range of applications — from manufacturing to service industries. Franka Robotics has integrated Isaac GR00T N1.5 into a dual-arm Franka Research 3robot for robotic control. The integration of GR00T N1.5 allows the system to interpret visual input, understand task context and autonomously perform complex manipulation — without the need for task-specific programming or hardcoded logic. Image courtesy of Franka Robotics. Hexagon, the global leader in measurement technologies, launched its new humanoid, dubbed AEON. With its unique locomotion system and multimodal sensor fusion, and powered by NVIDIA’s three-computer solution, AEON is engineered to perform a wide range of industrial applications, from manipulation and asset inspection to reality capture and operator support. Intrinsic, a software and AI robotics company, is integrating Intrinsic Flowstate with Omniverse and OpenUSD for advanced visualization and digital twins that can be used in many industrial use cases. The company is also using NVIDIA foundation models to enhance robot capabilities like grasp planning through AI and simulation technologies. SCHUNK, a global leader in gripping systems and automation technology, is showcasing its innovative grasping kit powered by the NVIDIA Jetson AGX Orin module. The kit intelligently detects objects and calculates optimal grasping points. Schunk is also demonstrating seamless simulation-to-reality transfer using IGS Virtuous software — built on Omniverse technologies — to control a real robot through simulation in a pick-and-place scenario. Universal Robots is showcasing UR15, its fastest cobot yet. Powered by the UR AI Accelerator — developed with NVIDIA and running on Jetson AGX Orin using CUDA-accelerated Isaac libraries — UR15 helps set a new standard for industrial automation. Vention, a full-stack software and hardware automation company, launched its Machine Motion AI, built on CUDA-accelerated Isaac libraries and powered by Jetson. Vention is also expanding its lineup of robotic offerings by adding the FR3 robot from Franka Robotics to its ecosystem, enhancing its solutions for academic and research applications. Image courtesy of Vention. Learn more about the latest robotics advancements by joining NVIDIA at Automatica, running through Friday, June 27. #nvidia #partners #highlight #nextgeneration #robotics

BLOGS.NVIDIA.COM

NVIDIA and Partners Highlight Next-Generation Robotics, Automation and AI Technologies at Automatica
From the heart of Germany’s automotive sector to manufacturing hubs across France and Italy, Europe is embracing industrial AI and advanced AI-powered robotics to address labor shortages, boost productivity and fuel sustainable economic growth. Robotics companies are developing humanoid robots and collaborative systems that integrate AI into real-world manufacturing applications. Supported by a $200 billion investment initiative and coordinated efforts from the European Commission, Europe is positioning itself at the forefront of the next wave of industrial automation, powered by AI. This momentum is on full display at Automatica — Europe’s premier conference on advancements in robotics, machine vision and intelligent manufacturing — taking place this week in Munich, Germany. NVIDIA and its ecosystem of partners and customers are showcasing next-generation robots, automation and AI technologies designed to accelerate the continent’s leadership in smart manufacturing and logistics. NVIDIA Technologies Boost Robotics Development Central to advancing robotics development is Europe’s first industrial AI cloud, announced at NVIDIA GTC Paris at VivaTech earlier this month. The Germany-based AI factory, featuring 10,000 NVIDIA GPUs, provides European manufacturers with secure, sovereign and centralized AI infrastructure for industrial workloads. It will support applications ranging from design and engineering to factory digital twins and robotics. To help accelerate humanoid development, NVIDIA released NVIDIA Isaac GR00T N1.5 — an open foundation model for humanoid robot reasoning and skills. This update enhances the model’s adaptability and ability to follow instructions, significantly improving its performance in material handling and manufacturing tasks. To help post-train GR00T N1.5, NVIDIA has also released the Isaac GR00T-Dreams blueprint — a reference workflow for generating vast amounts of synthetic trajectory data from a small number of human demonstrations — enabling robots to generalize across behaviors and adapt to new environments with minimal human demonstration data. In addition, early developer previews of NVIDIA Isaac Sim 5.0 and Isaac Lab 2.2 — open-source robot simulation and learning frameworks optimized for NVIDIA RTX PRO 6000 workstations — are now available on GitHub. Image courtesy of Wandelbots. Robotics Leaders Tap NVIDIA Simulation Technology to Develop and Deploy Humanoids and More Robotics developers and solutions providers across the globe are integrating NVIDIA’s three computers to train, simulate and deploy robots. NEURA Robotics, a German robotics company and pioneer for cognitive robots, unveiled the third generation of its humanoid, 4NE1, designed to assist humans in domestic and professional environments through advanced cognitive capabilities and humanlike interaction. 4NE1 is powered by GR00T N1 and was trained in Isaac Sim and Isaac Lab before real-world deployment. NEURA Robotics is also presenting Neuraverse, a digital twin and interconnected ecosystem for robot training, skills and applications, fully compatible with NVIDIA Omniverse technologies. Delta Electronics, a global leader in power management and smart green solutions, is debuting two next-generation collaborative robots: D-Bot Mar and D-Bot 2 in 1 — both trained using Omniverse and Isaac Sim technologies and libraries. These cobots are engineered to transform intralogistics and optimize production flows. Wandelbots, the creator of the Wandelbots NOVA software platform for industrial robotics, is partnering with SoftServe, a global IT consulting and digital services provider, to scale simulation-first automating using NVIDIA Isaac Sim, enabling virtual validation and real-world deployment with maximum impact. Cyngn, a pioneer in autonomous mobile robotics, is integrating its DriveMod technology into Isaac Sim to enable large-scale, high fidelity virtual testing of advanced autonomous operation. Purpose-built for industrial applications, DriveMod is already deployed on vehicles such as the Motrec MT-160 Tugger and BYD Forklift, delivering sophisticated automation to material handling operations. Doosan Robotics, a company specializing in AI robotic solutions, will showcase its “sim to real” solution, using NVIDIA Isaac Sim and cuRobo. Doosan will be showcasing how to seamlessly transfer tasks from simulation to real robots across a wide range of applications — from manufacturing to service industries. Franka Robotics has integrated Isaac GR00T N1.5 into a dual-arm Franka Research 3 (FR3) robot for robotic control. The integration of GR00T N1.5 allows the system to interpret visual input, understand task context and autonomously perform complex manipulation — without the need for task-specific programming or hardcoded logic. Image courtesy of Franka Robotics. Hexagon, the global leader in measurement technologies, launched its new humanoid, dubbed AEON. With its unique locomotion system and multimodal sensor fusion, and powered by NVIDIA’s three-computer solution, AEON is engineered to perform a wide range of industrial applications, from manipulation and asset inspection to reality capture and operator support. Intrinsic, a software and AI robotics company, is integrating Intrinsic Flowstate with Omniverse and OpenUSD for advanced visualization and digital twins that can be used in many industrial use cases. The company is also using NVIDIA foundation models to enhance robot capabilities like grasp planning through AI and simulation technologies. SCHUNK, a global leader in gripping systems and automation technology, is showcasing its innovative grasping kit powered by the NVIDIA Jetson AGX Orin module. The kit intelligently detects objects and calculates optimal grasping points. Schunk is also demonstrating seamless simulation-to-reality transfer using IGS Virtuous software — built on Omniverse technologies — to control a real robot through simulation in a pick-and-place scenario. Universal Robots is showcasing UR15, its fastest cobot yet. Powered by the UR AI Accelerator — developed with NVIDIA and running on Jetson AGX Orin using CUDA-accelerated Isaac libraries — UR15 helps set a new standard for industrial automation. Vention, a full-stack software and hardware automation company, launched its Machine Motion AI, built on CUDA-accelerated Isaac libraries and powered by Jetson. Vention is also expanding its lineup of robotic offerings by adding the FR3 robot from Franka Robotics to its ecosystem, enhancing its solutions for academic and research applications. Image courtesy of Vention. Learn more about the latest robotics advancements by joining NVIDIA at Automatica, running through Friday, June 27.

19

0 Comments 0 Shares

Please log in to like, share and comment!
MoEngage shared a link
2025-06-16 08:38:31 - Translate -

Ankur Kothari Q&A: Customer Engagement Book Interview

Reading Time: 9 minutes
In marketing, data isn’t a buzzword. It’s the lifeblood of all successful campaigns.
But are you truly harnessing its power, or are you drowning in a sea of information? To answer this question, we sat down with Ankur Kothari, a seasoned Martech expert, to dive deep into this crucial topic.
This interview, originally conducted for Chapter 6 of “The Customer Engagement Book: Adapt or Die” explores how businesses can translate raw data into actionable insights that drive real results.
Ankur shares his wealth of knowledge on identifying valuable customer engagement data, distinguishing between signal and noise, and ultimately, shaping real-time strategies that keep companies ahead of the curve.

Ankur Kothari Q&A Interview
1. What types of customer engagement data are most valuable for making strategic business decisions?
Primarily, there are four different buckets of customer engagement data. I would begin with behavioral data, encompassing website interaction, purchase history, and other app usage patterns.
Second would be demographic information: age, location, income, and other relevant personal characteristics.
Third would be sentiment analysis, where we derive information from social media interaction, customer feedback, or other customer reviews.
Fourth would be the customer journey data.

We track touchpoints across various channels of the customers to understand the customer journey path and conversion. Combining these four primary sources helps us understand the engagement data.

2. How do you distinguish between data that is actionable versus data that is just noise?
First is keeping relevant to your business objectives, making actionable data that directly relates to your specific goals or KPIs, and then taking help from statistical significance.
Actionable data shows clear patterns or trends that are statistically valid, whereas other data consists of random fluctuations or outliers, which may not be what you are interested in.

You also want to make sure that there is consistency across sources.
Actionable insights are typically corroborated by multiple data points or channels, while other data or noise can be more isolated and contradictory.
Actionable data suggests clear opportunities for improvement or decision making, whereas noise does not lead to meaningful actions or changes in strategy.

By applying these criteria, I can effectively filter out the noise and focus on data that delivers or drives valuable business decisions.

3. How can customer engagement data be used to identify and prioritize new business opportunities?
First, it helps us to uncover unmet needs.

By analyzing the customer feedback, touch points, support interactions, or usage patterns, we can identify the gaps in our current offerings or areas where customers are experiencing pain points.

Second would be identifying emerging needs.
Monitoring changes in customer behavior or preferences over time can reveal new market trends or shifts in demand, allowing my company to adapt their products or services accordingly.
Third would be segmentation analysis.
Detailed customer data analysis enables us to identify unserved or underserved segments or niche markets that may represent untapped opportunities for growth or expansion into newer areas and new geographies.
Last is to build competitive differentiation.

Engagement data can highlight where our companies outperform competitors, helping us to prioritize opportunities that leverage existing strengths and unique selling propositions.

4. Can you share an example of where data insights directly influenced a critical decision?
I will share an example from my previous organization at one of the financial services where we were very data-driven, which made a major impact on our critical decision regarding our credit card offerings.
We analyzed the customer engagement data, and we discovered that a large segment of our millennial customers were underutilizing our traditional credit cards but showed high engagement with mobile payment platforms.
That insight led us to develop and launch our first digital credit card product with enhanced mobile features and rewards tailored to the millennial spending habits. Since we had access to a lot of transactional data as well, we were able to build a financial product which met that specific segment’s needs.

That data-driven decision resulted in a 40% increase in our new credit card applications from this demographic within the first quarter of the launch. Subsequently, our market share improved in that specific segment, which was very crucial.

5. Are there any other examples of ways that you see customer engagement data being able to shape marketing strategy in real time?
When it comes to using the engagement data in real-time, we do quite a few things. In the recent past two, three years, we are using that for dynamic content personalization, adjusting the website content, email messaging, or ad creative based on real-time user behavior and preferences.
We automate campaign optimization using specific AI-driven tools to continuously analyze performance metrics and automatically reallocate the budget to top-performing channels or ad segments.
Then we also build responsive social media engagement platforms like monitoring social media sentiments and trending topics to quickly adapt the messaging and create timely and relevant content.

With one-on-one personalization, we do a lot of A/B testing as part of the overall rapid testing and market elements like subject lines, CTAs, and building various successful variants of the campaigns.

6. How are you doing the 1:1 personalization?
We have advanced CDP systems, and we are tracking each customer’s behavior in real-time. So the moment they move to different channels, we know what the context is, what the relevance is, and the recent interaction points, so we can cater the right offer.
So for example, if you looked at a certain offer on the website and you came from Google, and then the next day you walk into an in-person interaction, our agent will already know that you were looking at that offer.
That gives our customer or potential customer more one-to-one personalization instead of just segment-based or bulk interaction kind of experience.

We have a huge team of data scientists, data analysts, and AI model creators who help us to analyze big volumes of data and bring the right insights to our marketing and sales team so that they can provide the right experience to our customers.

7. What role does customer engagement data play in influencing cross-functional decisions, such as with product development, sales, and customer service?
Primarily with product development — we have different products, not just the financial products or products whichever organizations sell, but also various products like mobile apps or websites they use for transactions. So that kind of product development gets improved.
The engagement data helps our sales and marketing teams create more targeted campaigns, optimize channel selection, and refine messaging to resonate with specific customer segments.

Customer service also gets helped by anticipating common issues, personalizing support interactions over the phone or email or chat, and proactively addressing potential problems, leading to improved customer satisfaction and retention.

So in general, cross-functional application of engagement improves the customer-centric approach throughout the organization.

8. What do you think some of the main challenges marketers face when trying to translate customer engagement data into actionable business insights?
I think the huge amount of data we are dealing with. As we are getting more digitally savvy and most of the customers are moving to digital channels, we are getting a lot of data, and that sheer volume of data can be overwhelming, making it very difficult to identify truly meaningful patterns and insights.

Because of the huge data overload, we create data silos in this process, so information often exists in separate systems across different departments. We are not able to build a holistic view of customer engagement.

Because of data silos and overload of data, data quality issues appear. There is inconsistency, and inaccurate data can lead to incorrect insights or poor decision-making. Quality issues could also be due to the wrong format of the data, or the data is stale and no longer relevant.
As we are growing and adding more people to help us understand customer engagement, I’ve also noticed that technical folks, especially data scientists and data analysts, lack skills to properly interpret the data or apply data insights effectively.
So there’s a lack of understanding of marketing and sales as domains.
It’s a huge effort and can take a lot of investment.

Not being able to calculate the ROI of your overall investment is a big challenge that many organizations are facing.

9. Why do you think the analysts don’t have the business acumen to properly do more than analyze the data?
If people do not have the right idea of why we are collecting this data, we collect a lot of noise, and that brings in huge volumes of data. If you cannot stop that from step one—not bringing noise into the data system—that cannot be done by just technical folks or people who do not have business knowledge.
Business people do not know everything about what data is being collected from which source and what data they need. It’s a gap between business domain knowledge, specifically marketing and sales needs, and technical folks who don’t have a lot of exposure to that side.

Similarly, marketing business people do not have much exposure to the technical side — what’s possible to do with data, how much effort it takes, what’s relevant versus not relevant, and how to prioritize which data sources will be most important.

10. Do you have any suggestions for how this can be overcome, or have you seen it in action where it has been solved before?
First, cross-functional training: training different roles to help them understand why we’re doing this and what the business goals are, giving technical people exposure to what marketing and sales teams do.
And giving business folks exposure to the technology side through training on different tools, strategies, and the roadmap of data integrations.
The second is helping teams work more collaboratively. So it’s not like the technology team works in a silo and comes back when their work is done, and then marketing and sales teams act upon it.

Now we’re making it more like one team. You work together so that you can complement each other, and we have a better strategy from day one.

11. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations?
We present clear business cases where we demonstrate how data-driven recommendations can directly align with business objectives and potential ROI.
We build compelling visualizations, easy-to-understand charts and graphs that clearly illustrate the insights and the implications for business goals.

We also do a lot of POCs and pilot projects with small-scale implementations to showcase tangible results and build confidence in the data-driven approach throughout the organization.

12. What technologies or tools have you found most effective for gathering and analyzing customer engagement data?
I’ve found that Customer Data Platforms help us unify customer data from various sources, providing a comprehensive view of customer interactions across touch points.
Having advanced analytics platforms — tools with AI and machine learning capabilities that can process large volumes of data and uncover complex patterns and insights — is a great value to us.
We always use, or many organizations use, marketing automation systems to improve marketing team productivity, helping us track and analyze customer interactions across multiple channels.
Another thing is social media listening tools, wherever your brand is mentioned or you want to measure customer sentiment over social media, or track the engagement of your campaigns across social media platforms.

Last is web analytical tools, which provide detailed insights into your website visitors’ behaviors and engagement metrics, for browser apps, small browser apps, various devices, and mobile apps.

13. How do you ensure data quality and consistency across multiple channels to make these informed decisions?
We established clear guidelines for data collection, storage, and usage across all channels to maintain consistency. Then we use data integration platforms — tools that consolidate data from various sources into a single unified view, reducing discrepancies and inconsistencies.
While we collect data from different sources, we clean the data so it becomes cleaner with every stage of processing.
We also conduct regular data audits — performing periodic checks to identify and rectify data quality issues, ensuring accuracy and reliability of information. We also deploy standardized data formats.

On top of that, we have various automated data cleansing tools, specific software to detect and correct data errors, redundancies, duplicates, and inconsistencies in data sets automatically.

14. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years?
The first thing that’s been the biggest trend from the past two years is AI-driven decision making, which I think will become more prevalent, with advanced algorithms processing vast amounts of engagement data in real-time to inform strategic choices.
Somewhat related to this is predictive analytics, which will play an even larger role, enabling businesses to anticipate customer needs and market trends with more accuracy and better predictive capabilities.
We also touched upon hyper-personalization. We are all trying to strive toward more hyper-personalization at scale, which is more one-on-one personalization, as we are increasingly capturing more engagement data and have bigger systems and infrastructure to support processing those large volumes of data so we can achieve those hyper-personalization use cases.
As the world is collecting more data, privacy concerns and regulations come into play.
I believe in the next few years there will be more innovation toward how businesses can collect data ethically and what the usage practices are, leading to more transparent and consent-based engagement data strategies.
And lastly, I think about the integration of engagement data, which is always a big challenge. I believe as we’re solving those integration challenges, we are adding more and more complex data sources to the picture.

So I think there will need to be more innovation or sophistication brought into data integration strategies, which will help us take a truly customer-centric approach to strategy formulation.

This interview Q&A was hosted with Ankur Kothari, a previous Martech Executive, for Chapter 6 of The Customer Engagement Book: Adapt or Die.
Download the PDF or request a physical copy of the book here.
The post Ankur Kothari Q&A: Customer Engagement Book Interview appeared first on MoEngage.
#ankur #kothari #qampampa #customer #engagement

Ankur Kothari Q&A: Customer Engagement Book Interview
Reading Time: 9 minutes In marketing, data isn’t a buzzword. It’s the lifeblood of all successful campaigns. But are you truly harnessing its power, or are you drowning in a sea of information? To answer this question, we sat down with Ankur Kothari, a seasoned Martech expert, to dive deep into this crucial topic. This interview, originally conducted for Chapter 6 of “The Customer Engagement Book: Adapt or Die” explores how businesses can translate raw data into actionable insights that drive real results. Ankur shares his wealth of knowledge on identifying valuable customer engagement data, distinguishing between signal and noise, and ultimately, shaping real-time strategies that keep companies ahead of the curve. Ankur Kothari Q&A Interview 1. What types of customer engagement data are most valuable for making strategic business decisions? Primarily, there are four different buckets of customer engagement data. I would begin with behavioral data, encompassing website interaction, purchase history, and other app usage patterns. Second would be demographic information: age, location, income, and other relevant personal characteristics. Third would be sentiment analysis, where we derive information from social media interaction, customer feedback, or other customer reviews. Fourth would be the customer journey data. We track touchpoints across various channels of the customers to understand the customer journey path and conversion. Combining these four primary sources helps us understand the engagement data. 2. How do you distinguish between data that is actionable versus data that is just noise? First is keeping relevant to your business objectives, making actionable data that directly relates to your specific goals or KPIs, and then taking help from statistical significance. Actionable data shows clear patterns or trends that are statistically valid, whereas other data consists of random fluctuations or outliers, which may not be what you are interested in. You also want to make sure that there is consistency across sources. Actionable insights are typically corroborated by multiple data points or channels, while other data or noise can be more isolated and contradictory. Actionable data suggests clear opportunities for improvement or decision making, whereas noise does not lead to meaningful actions or changes in strategy. By applying these criteria, I can effectively filter out the noise and focus on data that delivers or drives valuable business decisions. 3. How can customer engagement data be used to identify and prioritize new business opportunities? First, it helps us to uncover unmet needs. By analyzing the customer feedback, touch points, support interactions, or usage patterns, we can identify the gaps in our current offerings or areas where customers are experiencing pain points. Second would be identifying emerging needs. Monitoring changes in customer behavior or preferences over time can reveal new market trends or shifts in demand, allowing my company to adapt their products or services accordingly. Third would be segmentation analysis. Detailed customer data analysis enables us to identify unserved or underserved segments or niche markets that may represent untapped opportunities for growth or expansion into newer areas and new geographies. Last is to build competitive differentiation. Engagement data can highlight where our companies outperform competitors, helping us to prioritize opportunities that leverage existing strengths and unique selling propositions. 4. Can you share an example of where data insights directly influenced a critical decision? I will share an example from my previous organization at one of the financial services where we were very data-driven, which made a major impact on our critical decision regarding our credit card offerings. We analyzed the customer engagement data, and we discovered that a large segment of our millennial customers were underutilizing our traditional credit cards but showed high engagement with mobile payment platforms. That insight led us to develop and launch our first digital credit card product with enhanced mobile features and rewards tailored to the millennial spending habits. Since we had access to a lot of transactional data as well, we were able to build a financial product which met that specific segment’s needs. That data-driven decision resulted in a 40% increase in our new credit card applications from this demographic within the first quarter of the launch. Subsequently, our market share improved in that specific segment, which was very crucial. 5. Are there any other examples of ways that you see customer engagement data being able to shape marketing strategy in real time? When it comes to using the engagement data in real-time, we do quite a few things. In the recent past two, three years, we are using that for dynamic content personalization, adjusting the website content, email messaging, or ad creative based on real-time user behavior and preferences. We automate campaign optimization using specific AI-driven tools to continuously analyze performance metrics and automatically reallocate the budget to top-performing channels or ad segments. Then we also build responsive social media engagement platforms like monitoring social media sentiments and trending topics to quickly adapt the messaging and create timely and relevant content. With one-on-one personalization, we do a lot of A/B testing as part of the overall rapid testing and market elements like subject lines, CTAs, and building various successful variants of the campaigns. 6. How are you doing the 1:1 personalization? We have advanced CDP systems, and we are tracking each customer’s behavior in real-time. So the moment they move to different channels, we know what the context is, what the relevance is, and the recent interaction points, so we can cater the right offer. So for example, if you looked at a certain offer on the website and you came from Google, and then the next day you walk into an in-person interaction, our agent will already know that you were looking at that offer. That gives our customer or potential customer more one-to-one personalization instead of just segment-based or bulk interaction kind of experience. We have a huge team of data scientists, data analysts, and AI model creators who help us to analyze big volumes of data and bring the right insights to our marketing and sales team so that they can provide the right experience to our customers. 7. What role does customer engagement data play in influencing cross-functional decisions, such as with product development, sales, and customer service? Primarily with product development — we have different products, not just the financial products or products whichever organizations sell, but also various products like mobile apps or websites they use for transactions. So that kind of product development gets improved. The engagement data helps our sales and marketing teams create more targeted campaigns, optimize channel selection, and refine messaging to resonate with specific customer segments. Customer service also gets helped by anticipating common issues, personalizing support interactions over the phone or email or chat, and proactively addressing potential problems, leading to improved customer satisfaction and retention. So in general, cross-functional application of engagement improves the customer-centric approach throughout the organization. 8. What do you think some of the main challenges marketers face when trying to translate customer engagement data into actionable business insights? I think the huge amount of data we are dealing with. As we are getting more digitally savvy and most of the customers are moving to digital channels, we are getting a lot of data, and that sheer volume of data can be overwhelming, making it very difficult to identify truly meaningful patterns and insights. Because of the huge data overload, we create data silos in this process, so information often exists in separate systems across different departments. We are not able to build a holistic view of customer engagement. Because of data silos and overload of data, data quality issues appear. There is inconsistency, and inaccurate data can lead to incorrect insights or poor decision-making. Quality issues could also be due to the wrong format of the data, or the data is stale and no longer relevant. As we are growing and adding more people to help us understand customer engagement, I’ve also noticed that technical folks, especially data scientists and data analysts, lack skills to properly interpret the data or apply data insights effectively. So there’s a lack of understanding of marketing and sales as domains. It’s a huge effort and can take a lot of investment. Not being able to calculate the ROI of your overall investment is a big challenge that many organizations are facing. 9. Why do you think the analysts don’t have the business acumen to properly do more than analyze the data? If people do not have the right idea of why we are collecting this data, we collect a lot of noise, and that brings in huge volumes of data. If you cannot stop that from step one—not bringing noise into the data system—that cannot be done by just technical folks or people who do not have business knowledge. Business people do not know everything about what data is being collected from which source and what data they need. It’s a gap between business domain knowledge, specifically marketing and sales needs, and technical folks who don’t have a lot of exposure to that side. Similarly, marketing business people do not have much exposure to the technical side — what’s possible to do with data, how much effort it takes, what’s relevant versus not relevant, and how to prioritize which data sources will be most important. 10. Do you have any suggestions for how this can be overcome, or have you seen it in action where it has been solved before? First, cross-functional training: training different roles to help them understand why we’re doing this and what the business goals are, giving technical people exposure to what marketing and sales teams do. And giving business folks exposure to the technology side through training on different tools, strategies, and the roadmap of data integrations. The second is helping teams work more collaboratively. So it’s not like the technology team works in a silo and comes back when their work is done, and then marketing and sales teams act upon it. Now we’re making it more like one team. You work together so that you can complement each other, and we have a better strategy from day one. 11. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations? We present clear business cases where we demonstrate how data-driven recommendations can directly align with business objectives and potential ROI. We build compelling visualizations, easy-to-understand charts and graphs that clearly illustrate the insights and the implications for business goals. We also do a lot of POCs and pilot projects with small-scale implementations to showcase tangible results and build confidence in the data-driven approach throughout the organization. 12. What technologies or tools have you found most effective for gathering and analyzing customer engagement data? I’ve found that Customer Data Platforms help us unify customer data from various sources, providing a comprehensive view of customer interactions across touch points. Having advanced analytics platforms — tools with AI and machine learning capabilities that can process large volumes of data and uncover complex patterns and insights — is a great value to us. We always use, or many organizations use, marketing automation systems to improve marketing team productivity, helping us track and analyze customer interactions across multiple channels. Another thing is social media listening tools, wherever your brand is mentioned or you want to measure customer sentiment over social media, or track the engagement of your campaigns across social media platforms. Last is web analytical tools, which provide detailed insights into your website visitors’ behaviors and engagement metrics, for browser apps, small browser apps, various devices, and mobile apps. 13. How do you ensure data quality and consistency across multiple channels to make these informed decisions? We established clear guidelines for data collection, storage, and usage across all channels to maintain consistency. Then we use data integration platforms — tools that consolidate data from various sources into a single unified view, reducing discrepancies and inconsistencies. While we collect data from different sources, we clean the data so it becomes cleaner with every stage of processing. We also conduct regular data audits — performing periodic checks to identify and rectify data quality issues, ensuring accuracy and reliability of information. We also deploy standardized data formats. On top of that, we have various automated data cleansing tools, specific software to detect and correct data errors, redundancies, duplicates, and inconsistencies in data sets automatically. 14. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years? The first thing that’s been the biggest trend from the past two years is AI-driven decision making, which I think will become more prevalent, with advanced algorithms processing vast amounts of engagement data in real-time to inform strategic choices. Somewhat related to this is predictive analytics, which will play an even larger role, enabling businesses to anticipate customer needs and market trends with more accuracy and better predictive capabilities. We also touched upon hyper-personalization. We are all trying to strive toward more hyper-personalization at scale, which is more one-on-one personalization, as we are increasingly capturing more engagement data and have bigger systems and infrastructure to support processing those large volumes of data so we can achieve those hyper-personalization use cases. As the world is collecting more data, privacy concerns and regulations come into play. I believe in the next few years there will be more innovation toward how businesses can collect data ethically and what the usage practices are, leading to more transparent and consent-based engagement data strategies. And lastly, I think about the integration of engagement data, which is always a big challenge. I believe as we’re solving those integration challenges, we are adding more and more complex data sources to the picture. So I think there will need to be more innovation or sophistication brought into data integration strategies, which will help us take a truly customer-centric approach to strategy formulation. This interview Q&A was hosted with Ankur Kothari, a previous Martech Executive, for Chapter 6 of The Customer Engagement Book: Adapt or Die. Download the PDF or request a physical copy of the book here. The post Ankur Kothari Q&A: Customer Engagement Book Interview appeared first on MoEngage. #ankur #kothari #qampampa #customer #engagement

WWW.MOENGAGE.COM

Ankur Kothari Q&A: Customer Engagement Book Interview
Reading Time: 9 minutes In marketing, data isn’t a buzzword. It’s the lifeblood of all successful campaigns. But are you truly harnessing its power, or are you drowning in a sea of information? To answer this question (and many others), we sat down with Ankur Kothari, a seasoned Martech expert, to dive deep into this crucial topic. This interview, originally conducted for Chapter 6 of “The Customer Engagement Book: Adapt or Die” explores how businesses can translate raw data into actionable insights that drive real results. Ankur shares his wealth of knowledge on identifying valuable customer engagement data, distinguishing between signal and noise, and ultimately, shaping real-time strategies that keep companies ahead of the curve. Ankur Kothari Q&A Interview 1. What types of customer engagement data are most valuable for making strategic business decisions? Primarily, there are four different buckets of customer engagement data. I would begin with behavioral data, encompassing website interaction, purchase history, and other app usage patterns. Second would be demographic information: age, location, income, and other relevant personal characteristics. Third would be sentiment analysis, where we derive information from social media interaction, customer feedback, or other customer reviews. Fourth would be the customer journey data. We track touchpoints across various channels of the customers to understand the customer journey path and conversion. Combining these four primary sources helps us understand the engagement data. 2. How do you distinguish between data that is actionable versus data that is just noise? First is keeping relevant to your business objectives, making actionable data that directly relates to your specific goals or KPIs, and then taking help from statistical significance. Actionable data shows clear patterns or trends that are statistically valid, whereas other data consists of random fluctuations or outliers, which may not be what you are interested in. You also want to make sure that there is consistency across sources. Actionable insights are typically corroborated by multiple data points or channels, while other data or noise can be more isolated and contradictory. Actionable data suggests clear opportunities for improvement or decision making, whereas noise does not lead to meaningful actions or changes in strategy. By applying these criteria, I can effectively filter out the noise and focus on data that delivers or drives valuable business decisions. 3. How can customer engagement data be used to identify and prioritize new business opportunities? First, it helps us to uncover unmet needs. By analyzing the customer feedback, touch points, support interactions, or usage patterns, we can identify the gaps in our current offerings or areas where customers are experiencing pain points. Second would be identifying emerging needs. Monitoring changes in customer behavior or preferences over time can reveal new market trends or shifts in demand, allowing my company to adapt their products or services accordingly. Third would be segmentation analysis. Detailed customer data analysis enables us to identify unserved or underserved segments or niche markets that may represent untapped opportunities for growth or expansion into newer areas and new geographies. Last is to build competitive differentiation. Engagement data can highlight where our companies outperform competitors, helping us to prioritize opportunities that leverage existing strengths and unique selling propositions. 4. Can you share an example of where data insights directly influenced a critical decision? I will share an example from my previous organization at one of the financial services where we were very data-driven, which made a major impact on our critical decision regarding our credit card offerings. We analyzed the customer engagement data, and we discovered that a large segment of our millennial customers were underutilizing our traditional credit cards but showed high engagement with mobile payment platforms. That insight led us to develop and launch our first digital credit card product with enhanced mobile features and rewards tailored to the millennial spending habits. Since we had access to a lot of transactional data as well, we were able to build a financial product which met that specific segment’s needs. That data-driven decision resulted in a 40% increase in our new credit card applications from this demographic within the first quarter of the launch. Subsequently, our market share improved in that specific segment, which was very crucial. 5. Are there any other examples of ways that you see customer engagement data being able to shape marketing strategy in real time? When it comes to using the engagement data in real-time, we do quite a few things. In the recent past two, three years, we are using that for dynamic content personalization, adjusting the website content, email messaging, or ad creative based on real-time user behavior and preferences. We automate campaign optimization using specific AI-driven tools to continuously analyze performance metrics and automatically reallocate the budget to top-performing channels or ad segments. Then we also build responsive social media engagement platforms like monitoring social media sentiments and trending topics to quickly adapt the messaging and create timely and relevant content. With one-on-one personalization, we do a lot of A/B testing as part of the overall rapid testing and market elements like subject lines, CTAs, and building various successful variants of the campaigns. 6. How are you doing the 1:1 personalization? We have advanced CDP systems, and we are tracking each customer’s behavior in real-time. So the moment they move to different channels, we know what the context is, what the relevance is, and the recent interaction points, so we can cater the right offer. So for example, if you looked at a certain offer on the website and you came from Google, and then the next day you walk into an in-person interaction, our agent will already know that you were looking at that offer. That gives our customer or potential customer more one-to-one personalization instead of just segment-based or bulk interaction kind of experience. We have a huge team of data scientists, data analysts, and AI model creators who help us to analyze big volumes of data and bring the right insights to our marketing and sales team so that they can provide the right experience to our customers. 7. What role does customer engagement data play in influencing cross-functional decisions, such as with product development, sales, and customer service? Primarily with product development — we have different products, not just the financial products or products whichever organizations sell, but also various products like mobile apps or websites they use for transactions. So that kind of product development gets improved. The engagement data helps our sales and marketing teams create more targeted campaigns, optimize channel selection, and refine messaging to resonate with specific customer segments. Customer service also gets helped by anticipating common issues, personalizing support interactions over the phone or email or chat, and proactively addressing potential problems, leading to improved customer satisfaction and retention. So in general, cross-functional application of engagement improves the customer-centric approach throughout the organization. 8. What do you think some of the main challenges marketers face when trying to translate customer engagement data into actionable business insights? I think the huge amount of data we are dealing with. As we are getting more digitally savvy and most of the customers are moving to digital channels, we are getting a lot of data, and that sheer volume of data can be overwhelming, making it very difficult to identify truly meaningful patterns and insights. Because of the huge data overload, we create data silos in this process, so information often exists in separate systems across different departments. We are not able to build a holistic view of customer engagement. Because of data silos and overload of data, data quality issues appear. There is inconsistency, and inaccurate data can lead to incorrect insights or poor decision-making. Quality issues could also be due to the wrong format of the data, or the data is stale and no longer relevant. As we are growing and adding more people to help us understand customer engagement, I’ve also noticed that technical folks, especially data scientists and data analysts, lack skills to properly interpret the data or apply data insights effectively. So there’s a lack of understanding of marketing and sales as domains. It’s a huge effort and can take a lot of investment. Not being able to calculate the ROI of your overall investment is a big challenge that many organizations are facing. 9. Why do you think the analysts don’t have the business acumen to properly do more than analyze the data? If people do not have the right idea of why we are collecting this data, we collect a lot of noise, and that brings in huge volumes of data. If you cannot stop that from step one—not bringing noise into the data system—that cannot be done by just technical folks or people who do not have business knowledge. Business people do not know everything about what data is being collected from which source and what data they need. It’s a gap between business domain knowledge, specifically marketing and sales needs, and technical folks who don’t have a lot of exposure to that side. Similarly, marketing business people do not have much exposure to the technical side — what’s possible to do with data, how much effort it takes, what’s relevant versus not relevant, and how to prioritize which data sources will be most important. 10. Do you have any suggestions for how this can be overcome, or have you seen it in action where it has been solved before? First, cross-functional training: training different roles to help them understand why we’re doing this and what the business goals are, giving technical people exposure to what marketing and sales teams do. And giving business folks exposure to the technology side through training on different tools, strategies, and the roadmap of data integrations. The second is helping teams work more collaboratively. So it’s not like the technology team works in a silo and comes back when their work is done, and then marketing and sales teams act upon it. Now we’re making it more like one team. You work together so that you can complement each other, and we have a better strategy from day one. 11. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations? We present clear business cases where we demonstrate how data-driven recommendations can directly align with business objectives and potential ROI. We build compelling visualizations, easy-to-understand charts and graphs that clearly illustrate the insights and the implications for business goals. We also do a lot of POCs and pilot projects with small-scale implementations to showcase tangible results and build confidence in the data-driven approach throughout the organization. 12. What technologies or tools have you found most effective for gathering and analyzing customer engagement data? I’ve found that Customer Data Platforms help us unify customer data from various sources, providing a comprehensive view of customer interactions across touch points. Having advanced analytics platforms — tools with AI and machine learning capabilities that can process large volumes of data and uncover complex patterns and insights — is a great value to us. We always use, or many organizations use, marketing automation systems to improve marketing team productivity, helping us track and analyze customer interactions across multiple channels. Another thing is social media listening tools, wherever your brand is mentioned or you want to measure customer sentiment over social media, or track the engagement of your campaigns across social media platforms. Last is web analytical tools, which provide detailed insights into your website visitors’ behaviors and engagement metrics, for browser apps, small browser apps, various devices, and mobile apps. 13. How do you ensure data quality and consistency across multiple channels to make these informed decisions? We established clear guidelines for data collection, storage, and usage across all channels to maintain consistency. Then we use data integration platforms — tools that consolidate data from various sources into a single unified view, reducing discrepancies and inconsistencies. While we collect data from different sources, we clean the data so it becomes cleaner with every stage of processing. We also conduct regular data audits — performing periodic checks to identify and rectify data quality issues, ensuring accuracy and reliability of information. We also deploy standardized data formats. On top of that, we have various automated data cleansing tools, specific software to detect and correct data errors, redundancies, duplicates, and inconsistencies in data sets automatically. 14. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years? The first thing that’s been the biggest trend from the past two years is AI-driven decision making, which I think will become more prevalent, with advanced algorithms processing vast amounts of engagement data in real-time to inform strategic choices. Somewhat related to this is predictive analytics, which will play an even larger role, enabling businesses to anticipate customer needs and market trends with more accuracy and better predictive capabilities. We also touched upon hyper-personalization. We are all trying to strive toward more hyper-personalization at scale, which is more one-on-one personalization, as we are increasingly capturing more engagement data and have bigger systems and infrastructure to support processing those large volumes of data so we can achieve those hyper-personalization use cases. As the world is collecting more data, privacy concerns and regulations come into play. I believe in the next few years there will be more innovation toward how businesses can collect data ethically and what the usage practices are, leading to more transparent and consent-based engagement data strategies. And lastly, I think about the integration of engagement data, which is always a big challenge. I believe as we’re solving those integration challenges, we are adding more and more complex data sources to the picture. So I think there will need to be more innovation or sophistication brought into data integration strategies, which will help us take a truly customer-centric approach to strategy formulation. This interview Q&A was hosted with Ankur Kothari, a previous Martech Executive, for Chapter 6 of The Customer Engagement Book: Adapt or Die. Download the PDF or request a physical copy of the book here. The post Ankur Kothari Q&A: Customer Engagement Book Interview appeared first on MoEngage.

478

0 Comments 0 Shares

Please log in to like, share and comment!
AI News shared a link
2025-06-16 08:12:36 - Translate -

The AI execution gap: Why 80% of projects don’t reach production

Enterprise artificial intelligence investment is unprecedented, with IDC projecting global spending on AI and GenAI to double to billion by 2028. Yet beneath the impressive budget allocations and boardroom enthusiasm lies a troubling reality: most organisations struggle to translate their AI ambitions into operational success.The sobering statistics behind AI’s promiseModelOp’s 2025 AI Governance Benchmark Report, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a disconnect between aspiration and execution.While more than 80% of enterprises have 51 or more generative AI projects in proposal phases, only 18% have successfully deployed more than 20 models into production.The execution gap represents one of the most significant challenges facing enterprise AI today. Most generative AI projects still require 6 to 18 months to go live – if they reach production at all.The result is delayed returns on investment, frustrated stakeholders, and diminished confidence in AI initiatives in the enterprise.The cause: Structural, not technical barriersThe biggest obstacles preventing AI scalability aren’t technical limitations – they’re structural inefficiencies plaguing enterprise operations. The ModelOp benchmark report identifies several problems that create what experts call a “time-to-market quagmire.”Fragmented systems plague implementation. 58% of organisations cite fragmented systems as the top obstacle to adopting governance platforms. Fragmentation creates silos where different departments use incompatible tools and processes, making it nearly impossible to maintain consistent oversight in AI initiatives.Manual processes dominate despite digital transformation. 55% of enterprises still rely on manual processes – including spreadsheets and email – to manage AI use case intake. The reliance on antiquated methods creates bottlenecks, increases the likelihood of errors, and makes it difficult to scale AI operations.Lack of standardisation hampers progress. Only 23% of organisations implement standardised intake, development, and model management processes. Without these elements, each AI project becomes a unique challenge requiring custom solutions and extensive coordination by multiple teams.Enterprise-level oversight remains rare Just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight. The lack of centralised governance means organisations often discover they’re solving the same problems multiple times in different departments.The governance revolution: From obstacle to acceleratorA change is taking place in how enterprises view AI governance. Rather than seeing it as a compliance burden that slows innovation, forward-thinking organisations recognise governance as an important enabler of scale and speed.Leadership alignment signals strategic shift. The ModelOp benchmark data reveals a change in organisational structure: 46% of companies now assign accountability for AI governance to a Chief Innovation Officer – more than four times the number who place accountability under Legal or Compliance. This strategic repositioning reflects a new understanding that governance isn’t solely about risk management, but can enable innovation.Investment follows strategic priority. A financial commitment to AI governance underscores its importance. According to the report, 36% of enterprises have budgeted at least million annually for AI governance software, while 54% have allocated resources specifically for AI Portfolio Intelligence to track value and ROI.What high-performing organisations do differentlyThe enterprises that successfully bridge the ‘execution gap’ share several characteristics in their approach to AI implementation:Standardised processes from day one. Leading organisations implement standardised intake, development, and model review processes in AI initiatives. Consistency eliminates the need to reinvent workflows for each project and ensures that all stakeholders understand their responsibilities.Centralised documentation and inventory. Rather than allowing AI assets to proliferate in disconnected systems, successful enterprises maintain centralised inventories that provide visibility into every model’s status, performance, and compliance posture.Automated governance checkpoints. High-performing organisations embed automated governance checkpoints throughout the AI lifecycle, helping ensure compliance requirements and risk assessments are addressed systematically rather than as afterthoughts.End-to-end traceability. Leading enterprises maintain complete traceability of their AI models, including data sources, training methods, validation results, and performance metrics.Measurable impact of structured governanceThe benefits of implementing comprehensive AI governance extend beyond compliance. Organisations that adopt lifecycle automation platforms reportedly see dramatic improvements in operational efficiency and business outcomes.A financial services firm profiled in the ModelOp report experienced a halving of time to production and an 80% reduction in issue resolution time after implementing automated governance processes. Such improvements translate directly into faster time-to-value and increased confidence among business stakeholders.Enterprises with robust governance frameworks report the ability to many times more models simultaneously while maintaining oversight and control. This scalability lets organisations pursue AI initiatives in multiple business units without overwhelming their operational capabilities.The path forward: From stuck to scaledThe message from industry leaders that the gap between AI ambition and execution is solvable, but it requires a shift in approach. Rather than treating governance as a necessary evil, enterprises should realise it enables AI innovation at scale.Immediate action items for AI leadersOrganisations looking to escape the ‘time-to-market quagmire’ should prioritise the following:Audit current state: Conduct an assessment of existing AI initiatives, identifying fragmented processes and manual bottlenecksStandardise workflows: Implement consistent processes for AI use case intake, development, and deployment in all business unitsInvest in integration: Deploy platforms to unify disparate tools and systems under a single governance frameworkEstablish enterprise oversight: Create centralised visibility into all AI initiatives with real-time monitoring and reporting abilitiesThe competitive advantage of getting it rightOrganisations that can solve the execution challenge will be able to bring AI solutions to market faster, scale more efficiently, and maintain the trust of stakeholders and regulators.Enterprises that continue with fragmented processes and manual workflows will find themselves disadvantaged compared to their more organised competitors. Operational excellence isn’t about efficiency but survival.The data shows enterprise AI investment will continue to grow. Therefore, the question isn’t whether organisations will invest in AI, but whether they’ll develop the operational abilities necessary to realise return on investment. The opportunity to lead in the AI-driven economy has never been greater for those willing to embrace governance as an enabler not an obstacle.
#execution #gap #why #projects #dont

The AI execution gap: Why 80% of projects don’t reach production
Enterprise artificial intelligence investment is unprecedented, with IDC projecting global spending on AI and GenAI to double to billion by 2028. Yet beneath the impressive budget allocations and boardroom enthusiasm lies a troubling reality: most organisations struggle to translate their AI ambitions into operational success.The sobering statistics behind AI’s promiseModelOp’s 2025 AI Governance Benchmark Report, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a disconnect between aspiration and execution.While more than 80% of enterprises have 51 or more generative AI projects in proposal phases, only 18% have successfully deployed more than 20 models into production.The execution gap represents one of the most significant challenges facing enterprise AI today. Most generative AI projects still require 6 to 18 months to go live – if they reach production at all.The result is delayed returns on investment, frustrated stakeholders, and diminished confidence in AI initiatives in the enterprise.The cause: Structural, not technical barriersThe biggest obstacles preventing AI scalability aren’t technical limitations – they’re structural inefficiencies plaguing enterprise operations. The ModelOp benchmark report identifies several problems that create what experts call a “time-to-market quagmire.”Fragmented systems plague implementation. 58% of organisations cite fragmented systems as the top obstacle to adopting governance platforms. Fragmentation creates silos where different departments use incompatible tools and processes, making it nearly impossible to maintain consistent oversight in AI initiatives.Manual processes dominate despite digital transformation. 55% of enterprises still rely on manual processes – including spreadsheets and email – to manage AI use case intake. The reliance on antiquated methods creates bottlenecks, increases the likelihood of errors, and makes it difficult to scale AI operations.Lack of standardisation hampers progress. Only 23% of organisations implement standardised intake, development, and model management processes. Without these elements, each AI project becomes a unique challenge requiring custom solutions and extensive coordination by multiple teams.Enterprise-level oversight remains rare Just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight. The lack of centralised governance means organisations often discover they’re solving the same problems multiple times in different departments.The governance revolution: From obstacle to acceleratorA change is taking place in how enterprises view AI governance. Rather than seeing it as a compliance burden that slows innovation, forward-thinking organisations recognise governance as an important enabler of scale and speed.Leadership alignment signals strategic shift. The ModelOp benchmark data reveals a change in organisational structure: 46% of companies now assign accountability for AI governance to a Chief Innovation Officer – more than four times the number who place accountability under Legal or Compliance. This strategic repositioning reflects a new understanding that governance isn’t solely about risk management, but can enable innovation.Investment follows strategic priority. A financial commitment to AI governance underscores its importance. According to the report, 36% of enterprises have budgeted at least million annually for AI governance software, while 54% have allocated resources specifically for AI Portfolio Intelligence to track value and ROI.What high-performing organisations do differentlyThe enterprises that successfully bridge the ‘execution gap’ share several characteristics in their approach to AI implementation:Standardised processes from day one. Leading organisations implement standardised intake, development, and model review processes in AI initiatives. Consistency eliminates the need to reinvent workflows for each project and ensures that all stakeholders understand their responsibilities.Centralised documentation and inventory. Rather than allowing AI assets to proliferate in disconnected systems, successful enterprises maintain centralised inventories that provide visibility into every model’s status, performance, and compliance posture.Automated governance checkpoints. High-performing organisations embed automated governance checkpoints throughout the AI lifecycle, helping ensure compliance requirements and risk assessments are addressed systematically rather than as afterthoughts.End-to-end traceability. Leading enterprises maintain complete traceability of their AI models, including data sources, training methods, validation results, and performance metrics.Measurable impact of structured governanceThe benefits of implementing comprehensive AI governance extend beyond compliance. Organisations that adopt lifecycle automation platforms reportedly see dramatic improvements in operational efficiency and business outcomes.A financial services firm profiled in the ModelOp report experienced a halving of time to production and an 80% reduction in issue resolution time after implementing automated governance processes. Such improvements translate directly into faster time-to-value and increased confidence among business stakeholders.Enterprises with robust governance frameworks report the ability to many times more models simultaneously while maintaining oversight and control. This scalability lets organisations pursue AI initiatives in multiple business units without overwhelming their operational capabilities.The path forward: From stuck to scaledThe message from industry leaders that the gap between AI ambition and execution is solvable, but it requires a shift in approach. Rather than treating governance as a necessary evil, enterprises should realise it enables AI innovation at scale.Immediate action items for AI leadersOrganisations looking to escape the ‘time-to-market quagmire’ should prioritise the following:Audit current state: Conduct an assessment of existing AI initiatives, identifying fragmented processes and manual bottlenecksStandardise workflows: Implement consistent processes for AI use case intake, development, and deployment in all business unitsInvest in integration: Deploy platforms to unify disparate tools and systems under a single governance frameworkEstablish enterprise oversight: Create centralised visibility into all AI initiatives with real-time monitoring and reporting abilitiesThe competitive advantage of getting it rightOrganisations that can solve the execution challenge will be able to bring AI solutions to market faster, scale more efficiently, and maintain the trust of stakeholders and regulators.Enterprises that continue with fragmented processes and manual workflows will find themselves disadvantaged compared to their more organised competitors. Operational excellence isn’t about efficiency but survival.The data shows enterprise AI investment will continue to grow. Therefore, the question isn’t whether organisations will invest in AI, but whether they’ll develop the operational abilities necessary to realise return on investment. The opportunity to lead in the AI-driven economy has never been greater for those willing to embrace governance as an enabler not an obstacle. #execution #gap #why #projects #dont

WWW.ARTIFICIALINTELLIGENCE-NEWS.COM

The AI execution gap: Why 80% of projects don’t reach production
Enterprise artificial intelligence investment is unprecedented, with IDC projecting global spending on AI and GenAI to double to $631 billion by 2028. Yet beneath the impressive budget allocations and boardroom enthusiasm lies a troubling reality: most organisations struggle to translate their AI ambitions into operational success.The sobering statistics behind AI’s promiseModelOp’s 2025 AI Governance Benchmark Report, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a disconnect between aspiration and execution.While more than 80% of enterprises have 51 or more generative AI projects in proposal phases, only 18% have successfully deployed more than 20 models into production.The execution gap represents one of the most significant challenges facing enterprise AI today. Most generative AI projects still require 6 to 18 months to go live – if they reach production at all.The result is delayed returns on investment, frustrated stakeholders, and diminished confidence in AI initiatives in the enterprise.The cause: Structural, not technical barriersThe biggest obstacles preventing AI scalability aren’t technical limitations – they’re structural inefficiencies plaguing enterprise operations. The ModelOp benchmark report identifies several problems that create what experts call a “time-to-market quagmire.”Fragmented systems plague implementation. 58% of organisations cite fragmented systems as the top obstacle to adopting governance platforms. Fragmentation creates silos where different departments use incompatible tools and processes, making it nearly impossible to maintain consistent oversight in AI initiatives.Manual processes dominate despite digital transformation. 55% of enterprises still rely on manual processes – including spreadsheets and email – to manage AI use case intake. The reliance on antiquated methods creates bottlenecks, increases the likelihood of errors, and makes it difficult to scale AI operations.Lack of standardisation hampers progress. Only 23% of organisations implement standardised intake, development, and model management processes. Without these elements, each AI project becomes a unique challenge requiring custom solutions and extensive coordination by multiple teams.Enterprise-level oversight remains rare Just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight. The lack of centralised governance means organisations often discover they’re solving the same problems multiple times in different departments.The governance revolution: From obstacle to acceleratorA change is taking place in how enterprises view AI governance. Rather than seeing it as a compliance burden that slows innovation, forward-thinking organisations recognise governance as an important enabler of scale and speed.Leadership alignment signals strategic shift. The ModelOp benchmark data reveals a change in organisational structure: 46% of companies now assign accountability for AI governance to a Chief Innovation Officer – more than four times the number who place accountability under Legal or Compliance. This strategic repositioning reflects a new understanding that governance isn’t solely about risk management, but can enable innovation.Investment follows strategic priority. A financial commitment to AI governance underscores its importance. According to the report, 36% of enterprises have budgeted at least $1 million annually for AI governance software, while 54% have allocated resources specifically for AI Portfolio Intelligence to track value and ROI.What high-performing organisations do differentlyThe enterprises that successfully bridge the ‘execution gap’ share several characteristics in their approach to AI implementation:Standardised processes from day one. Leading organisations implement standardised intake, development, and model review processes in AI initiatives. Consistency eliminates the need to reinvent workflows for each project and ensures that all stakeholders understand their responsibilities.Centralised documentation and inventory. Rather than allowing AI assets to proliferate in disconnected systems, successful enterprises maintain centralised inventories that provide visibility into every model’s status, performance, and compliance posture.Automated governance checkpoints. High-performing organisations embed automated governance checkpoints throughout the AI lifecycle, helping ensure compliance requirements and risk assessments are addressed systematically rather than as afterthoughts.End-to-end traceability. Leading enterprises maintain complete traceability of their AI models, including data sources, training methods, validation results, and performance metrics.Measurable impact of structured governanceThe benefits of implementing comprehensive AI governance extend beyond compliance. Organisations that adopt lifecycle automation platforms reportedly see dramatic improvements in operational efficiency and business outcomes.A financial services firm profiled in the ModelOp report experienced a halving of time to production and an 80% reduction in issue resolution time after implementing automated governance processes. Such improvements translate directly into faster time-to-value and increased confidence among business stakeholders.Enterprises with robust governance frameworks report the ability to many times more models simultaneously while maintaining oversight and control. This scalability lets organisations pursue AI initiatives in multiple business units without overwhelming their operational capabilities.The path forward: From stuck to scaledThe message from industry leaders that the gap between AI ambition and execution is solvable, but it requires a shift in approach. Rather than treating governance as a necessary evil, enterprises should realise it enables AI innovation at scale.Immediate action items for AI leadersOrganisations looking to escape the ‘time-to-market quagmire’ should prioritise the following:Audit current state: Conduct an assessment of existing AI initiatives, identifying fragmented processes and manual bottlenecksStandardise workflows: Implement consistent processes for AI use case intake, development, and deployment in all business unitsInvest in integration: Deploy platforms to unify disparate tools and systems under a single governance frameworkEstablish enterprise oversight: Create centralised visibility into all AI initiatives with real-time monitoring and reporting abilitiesThe competitive advantage of getting it rightOrganisations that can solve the execution challenge will be able to bring AI solutions to market faster, scale more efficiently, and maintain the trust of stakeholders and regulators.Enterprises that continue with fragmented processes and manual workflows will find themselves disadvantaged compared to their more organised competitors. Operational excellence isn’t about efficiency but survival.The data shows enterprise AI investment will continue to grow. Therefore, the question isn’t whether organisations will invest in AI, but whether they’ll develop the operational abilities necessary to realise return on investment. The opportunity to lead in the AI-driven economy has never been greater for those willing to embrace governance as an enabler not an obstacle.(Image source: Unsplash)

598

0 Comments 0 Shares

Please log in to like, share and comment!
3D Printing Industry shared a link
2025-06-16 07:50:16 - Translate -

New Court Order in Stratasys v. Bambu Lab Lawsuit

There has been a new update to the ongoing Stratasys v. Bambu Lab patent infringement lawsuit.
Both parties have agreed to consolidate the lead and member casesinto a single case under Case No. 2:25-cv-00465-JRG.
Industrial 3D printing OEM Stratasys filed the request late last month. According to an official court document, Shenzhen-based Bambu Lab did not oppose the motion. Stratasys argued that this non-opposition amounted to the defendants waiving their right to challenge the request under U.S. patent law 35 U.S.C. § 299.
On June 2, the U.S. District Court for the Eastern District of Texas, Marshall Division, ordered Bambu Lab to confirm in writing whether it agreed to the proposed case consolidation. The court took this step out of an “abundance of caution” to ensure both parties consented to the procedure before moving forward.
Bambu Lab submitted its response on June 12, agreeing to the consolidation. The company, along with co-defendants Shenzhen Tuozhu Technology Co., Ltd., Shanghai Lunkuo Technology Co., Ltd., and Tuozhu Technology Limited, waived its rights under 35 U.S.C. § 299. The court will now decide whether to merge the cases.
This followed U.S. District Judge Rodney Gilstrap’s decision last month to deny Bambu Lab’s motion to dismiss the lawsuits.
The Chinese desktop 3D printer manufacturer filed the motion in February 2025, arguing the cases were invalid because its US-based subsidiary, Bambu Lab USA, was not named in the original litigation. However, it agreed that the lawsuit could continue in the Austin division of the Western District of Texas, where a parallel case was filed last year.
Judge Gilstrap denied the motion, ruling that the cases properly target the named defendants. He concluded that Bambu Lab USA isn’t essential to the dispute, and that any misnaming should be addressed in summary judgment, not dismissal.
A Stratasys Fortus 450mcand a Bambu Lab X1C. Image by 3D Printing industry.
Another twist in the Stratasys v. Bambu Lab lawsuit
Stratasys filed the two lawsuits against Bambu Lab in the Eastern District of Texas, Marshall Division, in August 2024. The company claims that Bambu Lab’s X1C, X1E, P1S, P1P, A1, and A1 mini 3D printers violate ten of its patents. These patents cover common 3D printing features, including purge towers, heated build plates, tool head force detection, and networking capabilities.
Stratasys has requested a jury trial. It is seeking a ruling that Bambu Lab infringed its patents, along with financial damages and an injunction to stop Bambu from selling the allegedly infringing 3D printers.
Last October, Stratasys dropped charges against two of the originally named defendants in the dispute. Court documents showed that Beijing Tiertime Technology Co., Ltd. and Beijing Yinhua Laser Rapid Prototyping and Mould Technology Co., Ltd were removed. Both defendants represent the company Tiertime, China’s first 3D printer manufacturer. The District Court accepted the dismissal, with all claims dropped without prejudice.
It’s unclear why Stratasys named Beijing-based Tiertime as a defendant in the first place, given the lack of an obvious connection to Bambu Lab.
Tiertime and Stratasys have a history of legal disputes over patent issues. In 2013, Stratasys sued Afinia, Tiertime’s U.S. distributor and partner, for patent infringement. Afinia responded by suing uCRobotics, the Chinese distributor of MakerBot 3D printers, also alleging patent violations. Stratasys acquired MakerBot in June 2013. The company later merged with Ultimaker in 2022.
In February 2025, Bambu Lab filed a motion to dismiss the original lawsuits. The company argued that Stratasys’ claims, focused on the sale, importation, and distribution of 3D printers in the United States, do not apply to the Shenzhen-based parent company. Bambu Lab contended that the allegations concern its American subsidiary, Bambu Lab USA, which was not named in the complaint filed in the Eastern District of Texas.
Bambu Lab filed a motion to dismiss, claiming the case is invalid under Federal Rule of Civil Procedure 19. It argued that any party considered a “primary participant” in the allegations must be included as a defendant.
The court denied the motion on May 29, 2025. In the ruling, Judge Gilstrap explained that Stratasys’ allegations focus on the actions of the named defendants, not Bambu Lab USA. As a result, the official court document called Bambu Lab’s argument “unavailing.” Additionally, the Judge stated that, since Bambu Lab USA and Bambu Lab are both owned by Shenzhen Tuozhu, “the interest of these two entities align,” meaning the original cases are valid.
In the official court document, Judge Gilstrap emphasized that Stratasys can win or lose the lawsuits based solely on the actions of the current defendants, regardless of Bambu Lab USA’s involvement. He added that any potential risk to Bambu Lab USA’s business is too vague or hypothetical to justify making it a required party.
Finally, the court noted that even if Stratasys named the wrong defendant, this does not justify dismissal under Rule 12. Instead, the judge stated it would be more appropriate for the defendants to raise that argument in a motion for summary judgment.
The Bambu Lab X1C 3D printer. Image via Bambu Lab.
3D printing patent battles
The 3D printing industry has seen its fair share of patent infringement disputes over recent months. In May 2025, 3D printer hotend developer Slice Engineering reached an agreement with Creality over a patent non-infringement lawsuit.
The Chinese 3D printer OEM filed the lawsuit in July 2024 in the U.S. District Court for the Northern District of Florida, Gainesville Division. The company claimed that Slice Engineering had falsely accused it of infringing two hotend patents, U.S. Patent Nos. 10,875,244 and 11,660,810. These cover mechanical and thermal features of Slice’s Mosquito 3D printer hotend. Creality requested a jury trial and sought a ruling confirming it had not infringed either patent.
Court documents show that Slice Engineering filed a countersuit in December 2024. The Gainesville-based company maintained that Creaility “has infringed and continues to infringe” on both patents. In the filing, the company also denied allegations that it had harassed Creality’s partners, distributors, and customers, and claimed that Creality had refused to negotiate a resolution.
The Creality v. Slice Engineering lawsuit has since been dropped following a mutual resolution. Court documents show that both parties have permanently dismissed all claims and counterclaims, agreeing to cover their own legal fees and costs.
In other news, large-format resin 3D printer manufacturer Intrepid Automation sued 3D Systems over alleged patent infringement. The lawsuit, filed in February 2025, accused 3D Systems of using patented technology in its PSLA 270 industrial resin 3D printer. The filing called the PSLA 270 a “blatant knock off” of Intrepid’s DLP multi-projection “Range” 3D printer.
San Diego-based Intrepid Automation called this alleged infringement the “latest chapter of 3DS’s brazen, anticompetitive scheme to drive a smaller competitor with more advanced technology out of the marketplace.” The lawsuit also accused 3D Systems of corporate espionage, claiming one of its employees stole confidential trade secrets that were later used to develop the PSLA 270 printer.
3D Systems denied the allegations and filed a motion to dismiss the case. The company called the lawsuit “a desperate attempt” by Intrepid to distract from its own alleged theft of 3D Systems’ trade secrets.
Who won the 2024 3D Printing Industry Awards?
Subscribe to the 3D Printing Industry newsletter to keep up with the latest 3D printing news.You can also follow us on LinkedIn, and subscribe to the 3D Printing Industry Youtube channel to access more exclusive content.Featured image shows a Stratasys Fortus 450mcand a Bambu Lab X1C. Image by 3D Printing industry.
#new #court #order #stratasys #bambu

New Court Order in Stratasys v. Bambu Lab Lawsuit
There has been a new update to the ongoing Stratasys v. Bambu Lab patent infringement lawsuit. Both parties have agreed to consolidate the lead and member casesinto a single case under Case No. 2:25-cv-00465-JRG. Industrial 3D printing OEM Stratasys filed the request late last month. According to an official court document, Shenzhen-based Bambu Lab did not oppose the motion. Stratasys argued that this non-opposition amounted to the defendants waiving their right to challenge the request under U.S. patent law 35 U.S.C. § 299. On June 2, the U.S. District Court for the Eastern District of Texas, Marshall Division, ordered Bambu Lab to confirm in writing whether it agreed to the proposed case consolidation. The court took this step out of an “abundance of caution” to ensure both parties consented to the procedure before moving forward. Bambu Lab submitted its response on June 12, agreeing to the consolidation. The company, along with co-defendants Shenzhen Tuozhu Technology Co., Ltd., Shanghai Lunkuo Technology Co., Ltd., and Tuozhu Technology Limited, waived its rights under 35 U.S.C. § 299. The court will now decide whether to merge the cases. This followed U.S. District Judge Rodney Gilstrap’s decision last month to deny Bambu Lab’s motion to dismiss the lawsuits. The Chinese desktop 3D printer manufacturer filed the motion in February 2025, arguing the cases were invalid because its US-based subsidiary, Bambu Lab USA, was not named in the original litigation. However, it agreed that the lawsuit could continue in the Austin division of the Western District of Texas, where a parallel case was filed last year. Judge Gilstrap denied the motion, ruling that the cases properly target the named defendants. He concluded that Bambu Lab USA isn’t essential to the dispute, and that any misnaming should be addressed in summary judgment, not dismissal.        A Stratasys Fortus 450mcand a Bambu Lab X1C. Image by 3D Printing industry. Another twist in the Stratasys v. Bambu Lab lawsuit Stratasys filed the two lawsuits against Bambu Lab in the Eastern District of Texas, Marshall Division, in August 2024. The company claims that Bambu Lab’s X1C, X1E, P1S, P1P, A1, and A1 mini 3D printers violate ten of its patents. These patents cover common 3D printing features, including purge towers, heated build plates, tool head force detection, and networking capabilities. Stratasys has requested a jury trial. It is seeking a ruling that Bambu Lab infringed its patents, along with financial damages and an injunction to stop Bambu from selling the allegedly infringing 3D printers. Last October, Stratasys dropped charges against two of the originally named defendants in the dispute. Court documents showed that Beijing Tiertime Technology Co., Ltd. and Beijing Yinhua Laser Rapid Prototyping and Mould Technology Co., Ltd were removed. Both defendants represent the company Tiertime, China’s first 3D printer manufacturer. The District Court accepted the dismissal, with all claims dropped without prejudice. It’s unclear why Stratasys named Beijing-based Tiertime as a defendant in the first place, given the lack of an obvious connection to Bambu Lab. Tiertime and Stratasys have a history of legal disputes over patent issues. In 2013, Stratasys sued Afinia, Tiertime’s U.S. distributor and partner, for patent infringement. Afinia responded by suing uCRobotics, the Chinese distributor of MakerBot 3D printers, also alleging patent violations. Stratasys acquired MakerBot in June 2013. The company later merged with Ultimaker in 2022. In February 2025, Bambu Lab filed a motion to dismiss the original lawsuits. The company argued that Stratasys’ claims, focused on the sale, importation, and distribution of 3D printers in the United States, do not apply to the Shenzhen-based parent company. Bambu Lab contended that the allegations concern its American subsidiary, Bambu Lab USA, which was not named in the complaint filed in the Eastern District of Texas. Bambu Lab filed a motion to dismiss, claiming the case is invalid under Federal Rule of Civil Procedure 19. It argued that any party considered a “primary participant” in the allegations must be included as a defendant.    The court denied the motion on May 29, 2025. In the ruling, Judge Gilstrap explained that Stratasys’ allegations focus on the actions of the named defendants, not Bambu Lab USA. As a result, the official court document called Bambu Lab’s argument “unavailing.” Additionally, the Judge stated that, since Bambu Lab USA and Bambu Lab are both owned by Shenzhen Tuozhu, “the interest of these two entities align,” meaning the original cases are valid.   In the official court document, Judge Gilstrap emphasized that Stratasys can win or lose the lawsuits based solely on the actions of the current defendants, regardless of Bambu Lab USA’s involvement. He added that any potential risk to Bambu Lab USA’s business is too vague or hypothetical to justify making it a required party. Finally, the court noted that even if Stratasys named the wrong defendant, this does not justify dismissal under Rule 12. Instead, the judge stated it would be more appropriate for the defendants to raise that argument in a motion for summary judgment. The Bambu Lab X1C 3D printer. Image via Bambu Lab. 3D printing patent battles The 3D printing industry has seen its fair share of patent infringement disputes over recent months. In May 2025, 3D printer hotend developer Slice Engineering reached an agreement with Creality over a patent non-infringement lawsuit. The Chinese 3D printer OEM filed the lawsuit in July 2024 in the U.S. District Court for the Northern District of Florida, Gainesville Division. The company claimed that Slice Engineering had falsely accused it of infringing two hotend patents, U.S. Patent Nos. 10,875,244 and 11,660,810. These cover mechanical and thermal features of Slice’s Mosquito 3D printer hotend. Creality requested a jury trial and sought a ruling confirming it had not infringed either patent. Court documents show that Slice Engineering filed a countersuit in December 2024. The Gainesville-based company maintained that Creaility “has infringed and continues to infringe” on both patents. In the filing, the company also denied allegations that it had harassed Creality’s partners, distributors, and customers, and claimed that Creality had refused to negotiate a resolution.   The Creality v. Slice Engineering lawsuit has since been dropped following a mutual resolution. Court documents show that both parties have permanently dismissed all claims and counterclaims, agreeing to cover their own legal fees and costs. In other news, large-format resin 3D printer manufacturer Intrepid Automation sued 3D Systems over alleged patent infringement. The lawsuit, filed in February 2025, accused 3D Systems of using patented technology in its PSLA 270 industrial resin 3D printer. The filing called the PSLA 270 a “blatant knock off” of Intrepid’s DLP multi-projection “Range” 3D printer.   San Diego-based Intrepid Automation called this alleged infringement the “latest chapter of 3DS’s brazen, anticompetitive scheme to drive a smaller competitor with more advanced technology out of the marketplace.” The lawsuit also accused 3D Systems of corporate espionage, claiming one of its employees stole confidential trade secrets that were later used to develop the PSLA 270 printer. 3D Systems denied the allegations and filed a motion to dismiss the case. The company called the lawsuit “a desperate attempt” by Intrepid to distract from its own alleged theft of 3D Systems’ trade secrets. Who won the 2024 3D Printing Industry Awards? Subscribe to the 3D Printing Industry newsletter to keep up with the latest 3D printing news.You can also follow us on LinkedIn, and subscribe to the 3D Printing Industry Youtube channel to access more exclusive content.Featured image shows a Stratasys Fortus 450mcand a Bambu Lab X1C. Image by 3D Printing industry. #new #court #order #stratasys #bambu

3DPRINTINGINDUSTRY.COM

New Court Order in Stratasys v. Bambu Lab Lawsuit
There has been a new update to the ongoing Stratasys v. Bambu Lab patent infringement lawsuit. Both parties have agreed to consolidate the lead and member cases (2:24-CV-00644-JRG and 2:24-CV-00645-JRG) into a single case under Case No. 2:25-cv-00465-JRG. Industrial 3D printing OEM Stratasys filed the request late last month. According to an official court document, Shenzhen-based Bambu Lab did not oppose the motion. Stratasys argued that this non-opposition amounted to the defendants waiving their right to challenge the request under U.S. patent law 35 U.S.C. § 299(a). On June 2, the U.S. District Court for the Eastern District of Texas, Marshall Division, ordered Bambu Lab to confirm in writing whether it agreed to the proposed case consolidation. The court took this step out of an “abundance of caution” to ensure both parties consented to the procedure before moving forward. Bambu Lab submitted its response on June 12, agreeing to the consolidation. The company, along with co-defendants Shenzhen Tuozhu Technology Co., Ltd., Shanghai Lunkuo Technology Co., Ltd., and Tuozhu Technology Limited, waived its rights under 35 U.S.C. § 299(a). The court will now decide whether to merge the cases. This followed U.S. District Judge Rodney Gilstrap’s decision last month to deny Bambu Lab’s motion to dismiss the lawsuits. The Chinese desktop 3D printer manufacturer filed the motion in February 2025, arguing the cases were invalid because its US-based subsidiary, Bambu Lab USA, was not named in the original litigation. However, it agreed that the lawsuit could continue in the Austin division of the Western District of Texas, where a parallel case was filed last year. Judge Gilstrap denied the motion, ruling that the cases properly target the named defendants. He concluded that Bambu Lab USA isn’t essential to the dispute, and that any misnaming should be addressed in summary judgment, not dismissal.        A Stratasys Fortus 450mc (left) and a Bambu Lab X1C (right). Image by 3D Printing industry. Another twist in the Stratasys v. Bambu Lab lawsuit Stratasys filed the two lawsuits against Bambu Lab in the Eastern District of Texas, Marshall Division, in August 2024. The company claims that Bambu Lab’s X1C, X1E, P1S, P1P, A1, and A1 mini 3D printers violate ten of its patents. These patents cover common 3D printing features, including purge towers, heated build plates, tool head force detection, and networking capabilities. Stratasys has requested a jury trial. It is seeking a ruling that Bambu Lab infringed its patents, along with financial damages and an injunction to stop Bambu from selling the allegedly infringing 3D printers. Last October, Stratasys dropped charges against two of the originally named defendants in the dispute. Court documents showed that Beijing Tiertime Technology Co., Ltd. and Beijing Yinhua Laser Rapid Prototyping and Mould Technology Co., Ltd were removed. Both defendants represent the company Tiertime, China’s first 3D printer manufacturer. The District Court accepted the dismissal, with all claims dropped without prejudice. It’s unclear why Stratasys named Beijing-based Tiertime as a defendant in the first place, given the lack of an obvious connection to Bambu Lab. Tiertime and Stratasys have a history of legal disputes over patent issues. In 2013, Stratasys sued Afinia, Tiertime’s U.S. distributor and partner, for patent infringement. Afinia responded by suing uCRobotics, the Chinese distributor of MakerBot 3D printers, also alleging patent violations. Stratasys acquired MakerBot in June 2013. The company later merged with Ultimaker in 2022. In February 2025, Bambu Lab filed a motion to dismiss the original lawsuits. The company argued that Stratasys’ claims, focused on the sale, importation, and distribution of 3D printers in the United States, do not apply to the Shenzhen-based parent company. Bambu Lab contended that the allegations concern its American subsidiary, Bambu Lab USA, which was not named in the complaint filed in the Eastern District of Texas. Bambu Lab filed a motion to dismiss, claiming the case is invalid under Federal Rule of Civil Procedure 19. It argued that any party considered a “primary participant” in the allegations must be included as a defendant.    The court denied the motion on May 29, 2025. In the ruling, Judge Gilstrap explained that Stratasys’ allegations focus on the actions of the named defendants, not Bambu Lab USA. As a result, the official court document called Bambu Lab’s argument “unavailing.” Additionally, the Judge stated that, since Bambu Lab USA and Bambu Lab are both owned by Shenzhen Tuozhu, “the interest of these two entities align,” meaning the original cases are valid.   In the official court document, Judge Gilstrap emphasized that Stratasys can win or lose the lawsuits based solely on the actions of the current defendants, regardless of Bambu Lab USA’s involvement. He added that any potential risk to Bambu Lab USA’s business is too vague or hypothetical to justify making it a required party. Finally, the court noted that even if Stratasys named the wrong defendant, this does not justify dismissal under Rule 12(b)(7). Instead, the judge stated it would be more appropriate for the defendants to raise that argument in a motion for summary judgment. The Bambu Lab X1C 3D printer. Image via Bambu Lab. 3D printing patent battles The 3D printing industry has seen its fair share of patent infringement disputes over recent months. In May 2025, 3D printer hotend developer Slice Engineering reached an agreement with Creality over a patent non-infringement lawsuit. The Chinese 3D printer OEM filed the lawsuit in July 2024 in the U.S. District Court for the Northern District of Florida, Gainesville Division. The company claimed that Slice Engineering had falsely accused it of infringing two hotend patents, U.S. Patent Nos. 10,875,244 and 11,660,810. These cover mechanical and thermal features of Slice’s Mosquito 3D printer hotend. Creality requested a jury trial and sought a ruling confirming it had not infringed either patent. Court documents show that Slice Engineering filed a countersuit in December 2024. The Gainesville-based company maintained that Creaility “has infringed and continues to infringe” on both patents. In the filing, the company also denied allegations that it had harassed Creality’s partners, distributors, and customers, and claimed that Creality had refused to negotiate a resolution.   The Creality v. Slice Engineering lawsuit has since been dropped following a mutual resolution. Court documents show that both parties have permanently dismissed all claims and counterclaims, agreeing to cover their own legal fees and costs. In other news, large-format resin 3D printer manufacturer Intrepid Automation sued 3D Systems over alleged patent infringement. The lawsuit, filed in February 2025, accused 3D Systems of using patented technology in its PSLA 270 industrial resin 3D printer. The filing called the PSLA 270 a “blatant knock off” of Intrepid’s DLP multi-projection “Range” 3D printer.   San Diego-based Intrepid Automation called this alleged infringement the “latest chapter of 3DS’s brazen, anticompetitive scheme to drive a smaller competitor with more advanced technology out of the marketplace.” The lawsuit also accused 3D Systems of corporate espionage, claiming one of its employees stole confidential trade secrets that were later used to develop the PSLA 270 printer. 3D Systems denied the allegations and filed a motion to dismiss the case. The company called the lawsuit “a desperate attempt” by Intrepid to distract from its own alleged theft of 3D Systems’ trade secrets. Who won the 2024 3D Printing Industry Awards? Subscribe to the 3D Printing Industry newsletter to keep up with the latest 3D printing news.You can also follow us on LinkedIn, and subscribe to the 3D Printing Industry Youtube channel to access more exclusive content.Featured image shows a Stratasys Fortus 450mc (left) and a Bambu Lab X1C (right). Image by 3D Printing industry.

522

2 Comments 0 Shares

Please log in to like, share and comment!
InformationWeek shared a link
2025-06-16 07:42:24 - Translate -

The Role of the 3-2-1 Backup Rule in Cybersecurity

Daniel Pearson , CEO, KnownHostJune 12, 20253 Min ReadBusiness success concept. Cubes with arrows and target on the top.Cyber incidents are expected to cost the US billion in 2025. According to the latest estimates, this dynamic will continue to rise, reaching approximately 1.82 trillion US dollars in cybercrime costs by 2028. These figures highlight the crucial importance of strong cybersecurity strategies, which businesses must build to reduce the likelihood of risks. As technology evolves at a dramatic pace, businesses are increasingly dependent on utilizing digital infrastructure, exposing themselves to threats such as ransomware, accidental data loss, and corruption.  Despite the 3-2-1 backup rule being invented in 2009, this strategy has stayed relevant for businesses over the years, ensuring that the loss of data is minimized under threat, and will be a crucial method in the upcoming years to prevent major data loss.   What Is the 3-2-1 Backup Rule? The 3-2-1 backup rule is a popular backup strategy that ensures resilience against data loss. The setup consists of keeping your original data and two backups.  The data also needs to be stored in two different locations, such as the cloud or a local drive.  The one in the 3-2-1 backup rule represents storing a copy of your data off site, and this completes the setup.  This setup has been considered a gold standard in IT security, as it minimizes points of failure and increases the chance of successful data recovery in the event of a cyber-attack.  Related:Why Is This Rule Relevant in the Modern Cyber Threat Landscape? Statistics show that in 2024, 80% of companies have seen an increase in the frequency of cloud attacks.  Although many businesses assume that storing data in the cloud is enough, it is certainly not failsafe, and businesses are in bigger danger than ever due to the vast development of technology and AI capabilities attackers can manipulate and use.  As the cloud infrastructure has seen a similar speed of growth, cyber criminals are actively targeting these, leaving businesses with no clear recovery option. Therefore, more than ever, businesses need to invest in immutable backup solutions.  Common Backup Mistakes Businesses Make A common misstep is keeping all backups on the same physical network. If malware gets in, it can quickly spread and encrypt both the primary data and the backups, wiping out everything in one go. Another issue is the lack of offline or air-gapped backups. Many businesses rely entirely on cloud-based or on-premises storage that's always connected, which means their recovery options could be compromised during an attack. Related:Finally, one of the most overlooked yet crucial steps is testing backup restoration. A backup is only useful if it can actually be restored. Too often, companies skip regular testing. This can lead to a harsh reality check when they discover, too late, that their backup data is either corrupted or completely inaccessible after a breach. How to Implement the 3-2-1 Backup Rule? To successfully implement the 3-2-1 backup strategy as part of a robust cybersecurity framework, organizations should start by diversifying their storage methods. A resilient approach typically includes a mix of local storage, cloud-based solutions, and physical media such as external hard drives.  From there, it's essential to incorporate technologies that support write-once, read-many functionalities. This means backups cannot be modified or deleted, even by administrators, providing an extra layer of protection against threats. To further enhance resilience, organizations should make use of automation and AI-driven tools. These technologies can offer real-time monitoring, detect anomalies, and apply predictive analytics to maintain the integrity of backup data and flag any unusual activity or failures in the process. Lastly, it's crucial to ensure your backup strategy aligns with relevant regulatory requirements, such as GDPR in the UK or CCPA in the US. Compliance not only mitigates legal risk but also reinforces your commitment to data protection and operational continuity. Related:By blending the time-tested 3-2-1 rule with modern advances like immutable storage and intelligent monitoring, organizations can build a highly resilient backup architecture that strengthens their overall cybersecurity posture. About the AuthorDaniel Pearson CEO, KnownHostDaniel Pearson is the CEO of KnownHost, a managed web hosting service provider. Pearson also serves as a dedicated board member and supporter of the AlmaLinux OS Foundation, a non-profit organization focused on advancing the AlmaLinux OS -- an open-source operating system derived from RHEL. His passion for technology extends beyond his professional endeavors, as he actively promotes digital literacy and empowerment. Pearson's entrepreneurial drive and extensive industry knowledge have solidified his reputation as a respected figure in the tech community. See more from Daniel Pearson ReportsMore ReportsNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also Like
#role #backup #rule #cybersecurity

The Role of the 3-2-1 Backup Rule in Cybersecurity
Daniel Pearson , CEO, KnownHostJune 12, 20253 Min ReadBusiness success concept. Cubes with arrows and target on the top.Cyber incidents are expected to cost the US billion in 2025. According to the latest estimates, this dynamic will continue to rise, reaching approximately 1.82 trillion US dollars in cybercrime costs by 2028. These figures highlight the crucial importance of strong cybersecurity strategies, which businesses must build to reduce the likelihood of risks. As technology evolves at a dramatic pace, businesses are increasingly dependent on utilizing digital infrastructure, exposing themselves to threats such as ransomware, accidental data loss, and corruption.  Despite the 3-2-1 backup rule being invented in 2009, this strategy has stayed relevant for businesses over the years, ensuring that the loss of data is minimized under threat, and will be a crucial method in the upcoming years to prevent major data loss.   What Is the 3-2-1 Backup Rule? The 3-2-1 backup rule is a popular backup strategy that ensures resilience against data loss. The setup consists of keeping your original data and two backups.  The data also needs to be stored in two different locations, such as the cloud or a local drive.  The one in the 3-2-1 backup rule represents storing a copy of your data off site, and this completes the setup.  This setup has been considered a gold standard in IT security, as it minimizes points of failure and increases the chance of successful data recovery in the event of a cyber-attack.  Related:Why Is This Rule Relevant in the Modern Cyber Threat Landscape? Statistics show that in 2024, 80% of companies have seen an increase in the frequency of cloud attacks.  Although many businesses assume that storing data in the cloud is enough, it is certainly not failsafe, and businesses are in bigger danger than ever due to the vast development of technology and AI capabilities attackers can manipulate and use.  As the cloud infrastructure has seen a similar speed of growth, cyber criminals are actively targeting these, leaving businesses with no clear recovery option. Therefore, more than ever, businesses need to invest in immutable backup solutions.  Common Backup Mistakes Businesses Make A common misstep is keeping all backups on the same physical network. If malware gets in, it can quickly spread and encrypt both the primary data and the backups, wiping out everything in one go. Another issue is the lack of offline or air-gapped backups. Many businesses rely entirely on cloud-based or on-premises storage that's always connected, which means their recovery options could be compromised during an attack. Related:Finally, one of the most overlooked yet crucial steps is testing backup restoration. A backup is only useful if it can actually be restored. Too often, companies skip regular testing. This can lead to a harsh reality check when they discover, too late, that their backup data is either corrupted or completely inaccessible after a breach. How to Implement the 3-2-1 Backup Rule? To successfully implement the 3-2-1 backup strategy as part of a robust cybersecurity framework, organizations should start by diversifying their storage methods. A resilient approach typically includes a mix of local storage, cloud-based solutions, and physical media such as external hard drives.  From there, it's essential to incorporate technologies that support write-once, read-many functionalities. This means backups cannot be modified or deleted, even by administrators, providing an extra layer of protection against threats. To further enhance resilience, organizations should make use of automation and AI-driven tools. These technologies can offer real-time monitoring, detect anomalies, and apply predictive analytics to maintain the integrity of backup data and flag any unusual activity or failures in the process. Lastly, it's crucial to ensure your backup strategy aligns with relevant regulatory requirements, such as GDPR in the UK or CCPA in the US. Compliance not only mitigates legal risk but also reinforces your commitment to data protection and operational continuity. Related:By blending the time-tested 3-2-1 rule with modern advances like immutable storage and intelligent monitoring, organizations can build a highly resilient backup architecture that strengthens their overall cybersecurity posture. About the AuthorDaniel Pearson CEO, KnownHostDaniel Pearson is the CEO of KnownHost, a managed web hosting service provider. Pearson also serves as a dedicated board member and supporter of the AlmaLinux OS Foundation, a non-profit organization focused on advancing the AlmaLinux OS -- an open-source operating system derived from RHEL. His passion for technology extends beyond his professional endeavors, as he actively promotes digital literacy and empowerment. Pearson's entrepreneurial drive and extensive industry knowledge have solidified his reputation as a respected figure in the tech community. See more from Daniel Pearson ReportsMore ReportsNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also Like #role #backup #rule #cybersecurity

WWW.INFORMATIONWEEK.COM

The Role of the 3-2-1 Backup Rule in Cybersecurity
Daniel Pearson , CEO, KnownHostJune 12, 20253 Min ReadBusiness success concept. Cubes with arrows and target on the top.Cyber incidents are expected to cost the US $639 billion in 2025. According to the latest estimates, this dynamic will continue to rise, reaching approximately 1.82 trillion US dollars in cybercrime costs by 2028. These figures highlight the crucial importance of strong cybersecurity strategies, which businesses must build to reduce the likelihood of risks. As technology evolves at a dramatic pace, businesses are increasingly dependent on utilizing digital infrastructure, exposing themselves to threats such as ransomware, accidental data loss, and corruption.  Despite the 3-2-1 backup rule being invented in 2009, this strategy has stayed relevant for businesses over the years, ensuring that the loss of data is minimized under threat, and will be a crucial method in the upcoming years to prevent major data loss.   What Is the 3-2-1 Backup Rule? The 3-2-1 backup rule is a popular backup strategy that ensures resilience against data loss. The setup consists of keeping your original data and two backups.  The data also needs to be stored in two different locations, such as the cloud or a local drive.  The one in the 3-2-1 backup rule represents storing a copy of your data off site, and this completes the setup.  This setup has been considered a gold standard in IT security, as it minimizes points of failure and increases the chance of successful data recovery in the event of a cyber-attack.  Related:Why Is This Rule Relevant in the Modern Cyber Threat Landscape? Statistics show that in 2024, 80% of companies have seen an increase in the frequency of cloud attacks.  Although many businesses assume that storing data in the cloud is enough, it is certainly not failsafe, and businesses are in bigger danger than ever due to the vast development of technology and AI capabilities attackers can manipulate and use.  As the cloud infrastructure has seen a similar speed of growth, cyber criminals are actively targeting these, leaving businesses with no clear recovery option. Therefore, more than ever, businesses need to invest in immutable backup solutions.  Common Backup Mistakes Businesses Make A common misstep is keeping all backups on the same physical network. If malware gets in, it can quickly spread and encrypt both the primary data and the backups, wiping out everything in one go. Another issue is the lack of offline or air-gapped backups. Many businesses rely entirely on cloud-based or on-premises storage that's always connected, which means their recovery options could be compromised during an attack. Related:Finally, one of the most overlooked yet crucial steps is testing backup restoration. A backup is only useful if it can actually be restored. Too often, companies skip regular testing. This can lead to a harsh reality check when they discover, too late, that their backup data is either corrupted or completely inaccessible after a breach. How to Implement the 3-2-1 Backup Rule? To successfully implement the 3-2-1 backup strategy as part of a robust cybersecurity framework, organizations should start by diversifying their storage methods. A resilient approach typically includes a mix of local storage, cloud-based solutions, and physical media such as external hard drives.  From there, it's essential to incorporate technologies that support write-once, read-many functionalities. This means backups cannot be modified or deleted, even by administrators, providing an extra layer of protection against threats. To further enhance resilience, organizations should make use of automation and AI-driven tools. These technologies can offer real-time monitoring, detect anomalies, and apply predictive analytics to maintain the integrity of backup data and flag any unusual activity or failures in the process. Lastly, it's crucial to ensure your backup strategy aligns with relevant regulatory requirements, such as GDPR in the UK or CCPA in the US. Compliance not only mitigates legal risk but also reinforces your commitment to data protection and operational continuity. Related:By blending the time-tested 3-2-1 rule with modern advances like immutable storage and intelligent monitoring, organizations can build a highly resilient backup architecture that strengthens their overall cybersecurity posture. About the AuthorDaniel Pearson CEO, KnownHostDaniel Pearson is the CEO of KnownHost, a managed web hosting service provider. Pearson also serves as a dedicated board member and supporter of the AlmaLinux OS Foundation, a non-profit organization focused on advancing the AlmaLinux OS -- an open-source operating system derived from RHEL. His passion for technology extends beyond his professional endeavors, as he actively promotes digital literacy and empowerment. Pearson's entrepreneurial drive and extensive industry knowledge have solidified his reputation as a respected figure in the tech community. See more from Daniel Pearson ReportsMore ReportsNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also Like

519

2 Comments 0 Shares

Please log in to like, share and comment!
Creative Boom shared a link
2025-06-15 07:30:50 - Translate -

Inside the thinking behind Frontify Futures' standout brand identity

Who knows where branding will go in the future? However, for many of us working in the creative industries, it's our job to know. So it's something we need to start talking about, and Frontify Futures wants to be the platform where that conversation unfolds.
This ambitious new thought leadership initiative from Frontify brings together an extraordinary coalition of voices—CMOs who've scaled global brands, creative leaders reimagining possibilities, strategy directors pioneering new approaches, and cultural forecasters mapping emerging opportunities—to explore how effectiveness, innovation, and scale will shape tomorrow's brand-building landscape.
But Frontify Futures isn't just another content platform. Excitingly, from a design perspective, it's also a living experiment in what brand identity can become when technology meets craft, when systems embrace chaos, and when the future itself becomes a design material.
Endless variation
What makes Frontify Futures' typography unique isn't just its custom foundation: it's how that foundation enables endless variation and evolution. This was primarily achieved, reveals developer and digital art director Daniel Powell, by building bespoke tools for the project.

"Rather than rely solely on streamlined tools built for speed and production, we started building our own," he explains. "The first was a node-based design tool that takes our custom Frame and Hairline fonts as a base and uses them as the foundations for our type generator. With it, we can generate unique type variations for each content strand—each article, even—and create both static and animated type, exportable as video or rendered live in the browser."
Each of these tools included what Daniel calls a "chaos element: a small but intentional glitch in the system. A microstatement about the nature of the future: that it can be anticipated but never fully known. It's our way of keeping gesture alive inside the system."
One of the clearest examples of this is the colour palette generator. "It samples from a dynamic photo grid tied to a rotating colour wheel that completes one full revolution per year," Daniel explains. "But here's the twist: wind speed and direction in St. Gallen, Switzerland—Frontify's HQ—nudges the wheel unpredictably off-centre. It's a subtle, living mechanic; each article contains a log of the wind data in its code as a kind of Easter Egg."

Another favourite of Daniel's—yet to be released—is an expanded version of Conway's Game of Life. "It's been running continuously for over a month now, evolving patterns used in one of the content strand headers," he reveals. "The designer becomes a kind of photographer, capturing moments from a petri dish of generative motion."
Core Philosophy
In developing this unique identity, two phrases stood out to Daniel as guiding lights from the outset. The first was, 'We will show, not tell.'
"This became the foundation for how we approached the identity," recalls Daniel. "It had to feel like a playground: open, experimental, and fluid. Not overly precious or prescriptive. A system the Frontify team could truly own, shape, and evolve. A platform, not a final product. A foundation, just as the future is always built on the past."

The second guiding phrase, pulled directly from Frontify's rebrand materials, felt like "a call to action," says Daniel. "'Gestural and geometric. Human and machine. Art and science.' It's a tension that feels especially relevant in the creative industries today. As technology accelerates, we ask ourselves: how do we still hold onto our craft? What does it mean to be expressive in an increasingly systemised world?"
Stripped back and skeletal typography
The identity that Daniel and his team created reflects these themes through typography that literally embodies the platform's core philosophy. It really started from this idea of the past being built upon the 'foundations' of the past," he explains. "At the time Frontify Futures was being created, Frontify itself was going through a rebrand. With that, they'd started using a new variable typeface called Cranny, a custom cut of Azurio by Narrow Type."
Daniel's team took Cranny and "pushed it into a stripped-back and almost skeletal take". The result was Crany-Frame and Crany-Hairline. "These fonts then served as our base scaffolding," he continues. "They were never seen in design, but instead, we applied decoration them to produce new typefaces for each content strand, giving the identity the space to grow and allow new ideas and shapes to form."

As Daniel saw it, the demands on the typeface were pretty simple. "It needed to set an atmosphere. We needed it needed to feel alive. We wanted it to be something shifting and repositioning. And so, while we have a bunch of static cuts of each base style, we rarely use them; the typefaces you see on the website and social only exist at the moment as a string of parameters to create a general style that we use to create live animating versions of the font generated on the fly."
In addition to setting the atmosphere, it needed to be extremely flexible and feature live inputs, as a significant part of the branding is about the unpredictability of the future. "So Daniel's team built in those aforementioned "chaos moments where everything from user interaction to live windspeeds can affect the font."
Design Process
The process of creating the typefaces is a fascinating one. "We started by working with the custom cut of Azuriofrom Narrow Type. We then redrew it to take inspiration from how a frame and a hairline could be produced from this original cut. From there, we built a type generation tool that uses them as a base.
"It's a custom node-based system that lets us really get in there and play with the overlays for everything from grid-sizing, shapes and timing for the animation," he outlines. "We used this tool to design the variants for different content strands. We weren't just designing letterforms; we were designing a comprehensive toolset that could evolve in tandem with the content.
"That became a big part of the process: designing systems that designers could actually use, not just look at; again, it was a wider conversation and concept around the future and how designers and machines can work together."

In short, the evolution of the typeface system reflects the platform's broader commitment to continuous growth and adaptation." The whole idea was to make something open enough to keep building on," Daniel stresses. "We've already got tools in place to generate new weights, shapes and animated variants, and the tool itself still has a ton of unused functionality.
"I can see that growing as new content strands emerge; we'll keep adapting the type with them," he adds. "It's less about version numbers and more about ongoing movement. The system's alive; that's the point.
A provocation for the industry
In this context, the Frontify Futures identity represents more than smart visual branding; it's also a manifesto for how creative systems might evolve in an age of increasing automation and systematisation. By building unpredictability into their tools, embracing the tension between human craft and machine precision, and creating systems that grow and adapt rather than merely scale, Daniel and the Frontify team have created something that feels genuinely forward-looking.
For creatives grappling with similar questions about the future of their craft, Frontify Futures offers both inspiration and practical demonstration. It shows how brands can remain human while embracing technological capability, how systems can be both consistent and surprising, and how the future itself can become a creative medium.
This clever approach suggests that the future of branding lies not in choosing between human creativity and systematic efficiency but in finding new ways to make them work together, creating something neither could achieve alone.
#inside #thinking #behind #frontify #futures039

Inside the thinking behind Frontify Futures' standout brand identity
Who knows where branding will go in the future? However, for many of us working in the creative industries, it's our job to know. So it's something we need to start talking about, and Frontify Futures wants to be the platform where that conversation unfolds. This ambitious new thought leadership initiative from Frontify brings together an extraordinary coalition of voices—CMOs who've scaled global brands, creative leaders reimagining possibilities, strategy directors pioneering new approaches, and cultural forecasters mapping emerging opportunities—to explore how effectiveness, innovation, and scale will shape tomorrow's brand-building landscape. But Frontify Futures isn't just another content platform. Excitingly, from a design perspective, it's also a living experiment in what brand identity can become when technology meets craft, when systems embrace chaos, and when the future itself becomes a design material. Endless variation What makes Frontify Futures' typography unique isn't just its custom foundation: it's how that foundation enables endless variation and evolution. This was primarily achieved, reveals developer and digital art director Daniel Powell, by building bespoke tools for the project. "Rather than rely solely on streamlined tools built for speed and production, we started building our own," he explains. "The first was a node-based design tool that takes our custom Frame and Hairline fonts as a base and uses them as the foundations for our type generator. With it, we can generate unique type variations for each content strand—each article, even—and create both static and animated type, exportable as video or rendered live in the browser." Each of these tools included what Daniel calls a "chaos element: a small but intentional glitch in the system. A microstatement about the nature of the future: that it can be anticipated but never fully known. It's our way of keeping gesture alive inside the system." One of the clearest examples of this is the colour palette generator. "It samples from a dynamic photo grid tied to a rotating colour wheel that completes one full revolution per year," Daniel explains. "But here's the twist: wind speed and direction in St. Gallen, Switzerland—Frontify's HQ—nudges the wheel unpredictably off-centre. It's a subtle, living mechanic; each article contains a log of the wind data in its code as a kind of Easter Egg." Another favourite of Daniel's—yet to be released—is an expanded version of Conway's Game of Life. "It's been running continuously for over a month now, evolving patterns used in one of the content strand headers," he reveals. "The designer becomes a kind of photographer, capturing moments from a petri dish of generative motion." Core Philosophy In developing this unique identity, two phrases stood out to Daniel as guiding lights from the outset. The first was, 'We will show, not tell.' "This became the foundation for how we approached the identity," recalls Daniel. "It had to feel like a playground: open, experimental, and fluid. Not overly precious or prescriptive. A system the Frontify team could truly own, shape, and evolve. A platform, not a final product. A foundation, just as the future is always built on the past." The second guiding phrase, pulled directly from Frontify's rebrand materials, felt like "a call to action," says Daniel. "'Gestural and geometric. Human and machine. Art and science.' It's a tension that feels especially relevant in the creative industries today. As technology accelerates, we ask ourselves: how do we still hold onto our craft? What does it mean to be expressive in an increasingly systemised world?" Stripped back and skeletal typography The identity that Daniel and his team created reflects these themes through typography that literally embodies the platform's core philosophy. It really started from this idea of the past being built upon the 'foundations' of the past," he explains. "At the time Frontify Futures was being created, Frontify itself was going through a rebrand. With that, they'd started using a new variable typeface called Cranny, a custom cut of Azurio by Narrow Type." Daniel's team took Cranny and "pushed it into a stripped-back and almost skeletal take". The result was Crany-Frame and Crany-Hairline. "These fonts then served as our base scaffolding," he continues. "They were never seen in design, but instead, we applied decoration them to produce new typefaces for each content strand, giving the identity the space to grow and allow new ideas and shapes to form." As Daniel saw it, the demands on the typeface were pretty simple. "It needed to set an atmosphere. We needed it needed to feel alive. We wanted it to be something shifting and repositioning. And so, while we have a bunch of static cuts of each base style, we rarely use them; the typefaces you see on the website and social only exist at the moment as a string of parameters to create a general style that we use to create live animating versions of the font generated on the fly." In addition to setting the atmosphere, it needed to be extremely flexible and feature live inputs, as a significant part of the branding is about the unpredictability of the future. "So Daniel's team built in those aforementioned "chaos moments where everything from user interaction to live windspeeds can affect the font." Design Process The process of creating the typefaces is a fascinating one. "We started by working with the custom cut of Azuriofrom Narrow Type. We then redrew it to take inspiration from how a frame and a hairline could be produced from this original cut. From there, we built a type generation tool that uses them as a base. "It's a custom node-based system that lets us really get in there and play with the overlays for everything from grid-sizing, shapes and timing for the animation," he outlines. "We used this tool to design the variants for different content strands. We weren't just designing letterforms; we were designing a comprehensive toolset that could evolve in tandem with the content. "That became a big part of the process: designing systems that designers could actually use, not just look at; again, it was a wider conversation and concept around the future and how designers and machines can work together." In short, the evolution of the typeface system reflects the platform's broader commitment to continuous growth and adaptation." The whole idea was to make something open enough to keep building on," Daniel stresses. "We've already got tools in place to generate new weights, shapes and animated variants, and the tool itself still has a ton of unused functionality. "I can see that growing as new content strands emerge; we'll keep adapting the type with them," he adds. "It's less about version numbers and more about ongoing movement. The system's alive; that's the point. A provocation for the industry In this context, the Frontify Futures identity represents more than smart visual branding; it's also a manifesto for how creative systems might evolve in an age of increasing automation and systematisation. By building unpredictability into their tools, embracing the tension between human craft and machine precision, and creating systems that grow and adapt rather than merely scale, Daniel and the Frontify team have created something that feels genuinely forward-looking. For creatives grappling with similar questions about the future of their craft, Frontify Futures offers both inspiration and practical demonstration. It shows how brands can remain human while embracing technological capability, how systems can be both consistent and surprising, and how the future itself can become a creative medium. This clever approach suggests that the future of branding lies not in choosing between human creativity and systematic efficiency but in finding new ways to make them work together, creating something neither could achieve alone. #inside #thinking #behind #frontify #futures039

WWW.CREATIVEBOOM.COM

Inside the thinking behind Frontify Futures' standout brand identity
Who knows where branding will go in the future? However, for many of us working in the creative industries, it's our job to know. So it's something we need to start talking about, and Frontify Futures wants to be the platform where that conversation unfolds. This ambitious new thought leadership initiative from Frontify brings together an extraordinary coalition of voices—CMOs who've scaled global brands, creative leaders reimagining possibilities, strategy directors pioneering new approaches, and cultural forecasters mapping emerging opportunities—to explore how effectiveness, innovation, and scale will shape tomorrow's brand-building landscape. But Frontify Futures isn't just another content platform. Excitingly, from a design perspective, it's also a living experiment in what brand identity can become when technology meets craft, when systems embrace chaos, and when the future itself becomes a design material. Endless variation What makes Frontify Futures' typography unique isn't just its custom foundation: it's how that foundation enables endless variation and evolution. This was primarily achieved, reveals developer and digital art director Daniel Powell, by building bespoke tools for the project. "Rather than rely solely on streamlined tools built for speed and production, we started building our own," he explains. "The first was a node-based design tool that takes our custom Frame and Hairline fonts as a base and uses them as the foundations for our type generator. With it, we can generate unique type variations for each content strand—each article, even—and create both static and animated type, exportable as video or rendered live in the browser." Each of these tools included what Daniel calls a "chaos element: a small but intentional glitch in the system. A microstatement about the nature of the future: that it can be anticipated but never fully known. It's our way of keeping gesture alive inside the system." One of the clearest examples of this is the colour palette generator. "It samples from a dynamic photo grid tied to a rotating colour wheel that completes one full revolution per year," Daniel explains. "But here's the twist: wind speed and direction in St. Gallen, Switzerland—Frontify's HQ—nudges the wheel unpredictably off-centre. It's a subtle, living mechanic; each article contains a log of the wind data in its code as a kind of Easter Egg." Another favourite of Daniel's—yet to be released—is an expanded version of Conway's Game of Life. "It's been running continuously for over a month now, evolving patterns used in one of the content strand headers," he reveals. "The designer becomes a kind of photographer, capturing moments from a petri dish of generative motion." Core Philosophy In developing this unique identity, two phrases stood out to Daniel as guiding lights from the outset. The first was, 'We will show, not tell.' "This became the foundation for how we approached the identity," recalls Daniel. "It had to feel like a playground: open, experimental, and fluid. Not overly precious or prescriptive. A system the Frontify team could truly own, shape, and evolve. A platform, not a final product. A foundation, just as the future is always built on the past." The second guiding phrase, pulled directly from Frontify's rebrand materials, felt like "a call to action," says Daniel. "'Gestural and geometric. Human and machine. Art and science.' It's a tension that feels especially relevant in the creative industries today. As technology accelerates, we ask ourselves: how do we still hold onto our craft? What does it mean to be expressive in an increasingly systemised world?" Stripped back and skeletal typography The identity that Daniel and his team created reflects these themes through typography that literally embodies the platform's core philosophy. It really started from this idea of the past being built upon the 'foundations' of the past," he explains. "At the time Frontify Futures was being created, Frontify itself was going through a rebrand. With that, they'd started using a new variable typeface called Cranny, a custom cut of Azurio by Narrow Type." Daniel's team took Cranny and "pushed it into a stripped-back and almost skeletal take". The result was Crany-Frame and Crany-Hairline. "These fonts then served as our base scaffolding," he continues. "They were never seen in design, but instead, we applied decoration them to produce new typefaces for each content strand, giving the identity the space to grow and allow new ideas and shapes to form." As Daniel saw it, the demands on the typeface were pretty simple. "It needed to set an atmosphere. We needed it needed to feel alive. We wanted it to be something shifting and repositioning. And so, while we have a bunch of static cuts of each base style, we rarely use them; the typefaces you see on the website and social only exist at the moment as a string of parameters to create a general style that we use to create live animating versions of the font generated on the fly." In addition to setting the atmosphere, it needed to be extremely flexible and feature live inputs, as a significant part of the branding is about the unpredictability of the future. "So Daniel's team built in those aforementioned "chaos moments where everything from user interaction to live windspeeds can affect the font." Design Process The process of creating the typefaces is a fascinating one. "We started by working with the custom cut of Azurio (Cranny) from Narrow Type. We then redrew it to take inspiration from how a frame and a hairline could be produced from this original cut. From there, we built a type generation tool that uses them as a base. "It's a custom node-based system that lets us really get in there and play with the overlays for everything from grid-sizing, shapes and timing for the animation," he outlines. "We used this tool to design the variants for different content strands. We weren't just designing letterforms; we were designing a comprehensive toolset that could evolve in tandem with the content. "That became a big part of the process: designing systems that designers could actually use, not just look at; again, it was a wider conversation and concept around the future and how designers and machines can work together." In short, the evolution of the typeface system reflects the platform's broader commitment to continuous growth and adaptation." The whole idea was to make something open enough to keep building on," Daniel stresses. "We've already got tools in place to generate new weights, shapes and animated variants, and the tool itself still has a ton of unused functionality. "I can see that growing as new content strands emerge; we'll keep adapting the type with them," he adds. "It's less about version numbers and more about ongoing movement. The system's alive; that's the point. A provocation for the industry In this context, the Frontify Futures identity represents more than smart visual branding; it's also a manifesto for how creative systems might evolve in an age of increasing automation and systematisation. By building unpredictability into their tools, embracing the tension between human craft and machine precision, and creating systems that grow and adapt rather than merely scale, Daniel and the Frontify team have created something that feels genuinely forward-looking. For creatives grappling with similar questions about the future of their craft, Frontify Futures offers both inspiration and practical demonstration. It shows how brands can remain human while embracing technological capability, how systems can be both consistent and surprising, and how the future itself can become a creative medium. This clever approach suggests that the future of branding lies not in choosing between human creativity and systematic efficiency but in finding new ways to make them work together, creating something neither could achieve alone.

0 Comments 0 Shares

Please log in to like, share and comment!
EasyDMARC shared a link
2025-06-15 07:28:31 - Translate -

EasyDMARC Integrates With Pax8 Marketplace To Simplify Email Security For MSPs

Originally published at EasyDMARC Integrates With Pax8 Marketplace To Simplify Email Security For MSPs by Anush Yolyan.

The integration will deliver simple, accessible, and streamlined email security for vulnerable inboxes

Global, 4 November 2024 – US-based email security firm EasyDMARC has today announced its integration with Pax8 Marketplace, the leading cloud commerce marketplace. As one of the first DMARC solution providers on the Pax8 Marketplace, EasyDMARC is expanding its mission to protect inboxes from the rising threat of phishing attacks with a rigorous, user-friendly DMARC solution.

The integration comes as Google highlights the impressive results of recently implemented email authentication measures for bulk senders: a 65% reduction in unauthenticated messages to Gmail users, a 50% increase in bulk senders following best security practices, and 265 billion fewer unauthenticated messages sent in 2024. With email being such a crucial communication channel for businesses, email authentication measures are an essential part of any business’s cybersecurity offering.

Key features of the integration include:

Centralized billing

With centralized billing, customers can now streamline their cloud services under a single pane of glass, simplifying the management and billing of their EasyDMARC solution. This consolidated approach enables partners to reduce administrative complexity and manage all cloud expenses through one interface, providing a seamless billing and support experience.

Automated provisioning

Through automated provisioning, Pax8’s automation capabilities make deploying DMARC across client accounts quick and hassle-free. By eliminating manual configurations, this integration ensures that customers can implement email security solutions rapidly, allowing them to safeguard client inboxes without delay.

Bundled offerings

The bundled offerings available through Pax8 allow partners to enhance their service portfolios by combining EasyDMARC with complementary security solutions. By creating all-in-one security packages, partners can offer their clients more robust protection, addressing a broader range of security needs from a single, trusted platform.

Gerasim Hovhannisyan, Co-Founder and CEO of EasyDMARC, said:

“We’re thrilled to be working with Pax8 to provide MSPs with a streamlined, effective way to deliver top-tier email security to their clients, all within a platform that equips them with everything needed to stay secure. As phishing attacks grow in frequency and sophistication, businesses can no longer afford to overlook the importance of email security. Email authentication is a vital defense against the evolving threat of phishing and is crucial in preserving the integrity of email communication. This integration is designed to allow businesses of all sizes to benefit from DMARC’s extensive capabilities.”

Ryan Burton, Vice President of Marketplace Vendor Strategy, at Pax8 said:

“We’re delighted to welcome EasyDMARC to the Pax8 Marketplace as an enterprise-class DMARC solution provider. This integration gives MSPs the tools they need to meet the growing demand for email security, with simplified deployment, billing, and bundling benefits. With EasyDMARC’s technical capabilities and intelligence, MSPs can deliver robust protection against phishing threats without the technical hassle that often holds businesses back.”

About EasyDMARC

EasyDMARC is a cloud-native B2B SaaS solution that addresses email security and deliverability problems with just a few clicks. For Managed Service Providers seeking to increase their revenue, EasyDMARC presents an ideal solution. The email authentication platform streamlines domain management, providing capabilities such as organizational control, domain grouping, and access management.

Additionally, EasyDMARC offers a comprehensive sales and marketing enablement program designed to boost DMARC sales. All of these features are available for MSPs on a scalable platform with a flexible pay-as-you-go pricing model.

For more information on the EasyDMARC, visit: /

About Pax8

Pax8 is the technology marketplace of the future, linking partners, vendors, and small to midsized businessesthrough AI-powered insights and comprehensive product support. With a global partner ecosystem of over 38,000 managed service providers, Pax8 empowers SMBs worldwide by providing software and services that unlock their growth potential and enhance their security. Committed to innovating cloud commerce at scale, Pax8 drives customer acquisition and solution consumption across its entire ecosystem.

Find out more: /

The post EasyDMARC Integrates With Pax8 Marketplace To Simplify Email Security For MSPs appeared first on EasyDMARC.
#easydmarc #integrates #with #pax8 #marketplace

EasyDMARC Integrates With Pax8 Marketplace To Simplify Email Security For MSPs
Originally published at EasyDMARC Integrates With Pax8 Marketplace To Simplify Email Security For MSPs by Anush Yolyan. The integration will deliver simple, accessible, and streamlined email security for vulnerable inboxes Global, 4 November 2024 – US-based email security firm EasyDMARC has today announced its integration with Pax8 Marketplace, the leading cloud commerce marketplace. As one of the first DMARC solution providers on the Pax8 Marketplace, EasyDMARC is expanding its mission to protect inboxes from the rising threat of phishing attacks with a rigorous, user-friendly DMARC solution. The integration comes as Google highlights the impressive results of recently implemented email authentication measures for bulk senders: a 65% reduction in unauthenticated messages to Gmail users, a 50% increase in bulk senders following best security practices, and 265 billion fewer unauthenticated messages sent in 2024. With email being such a crucial communication channel for businesses, email authentication measures are an essential part of any business’s cybersecurity offering. Key features of the integration include: Centralized billing With centralized billing, customers can now streamline their cloud services under a single pane of glass, simplifying the management and billing of their EasyDMARC solution. This consolidated approach enables partners to reduce administrative complexity and manage all cloud expenses through one interface, providing a seamless billing and support experience. Automated provisioning Through automated provisioning, Pax8’s automation capabilities make deploying DMARC across client accounts quick and hassle-free. By eliminating manual configurations, this integration ensures that customers can implement email security solutions rapidly, allowing them to safeguard client inboxes without delay. Bundled offerings The bundled offerings available through Pax8 allow partners to enhance their service portfolios by combining EasyDMARC with complementary security solutions. By creating all-in-one security packages, partners can offer their clients more robust protection, addressing a broader range of security needs from a single, trusted platform. Gerasim Hovhannisyan, Co-Founder and CEO of EasyDMARC, said: “We’re thrilled to be working with Pax8 to provide MSPs with a streamlined, effective way to deliver top-tier email security to their clients, all within a platform that equips them with everything needed to stay secure. As phishing attacks grow in frequency and sophistication, businesses can no longer afford to overlook the importance of email security. Email authentication is a vital defense against the evolving threat of phishing and is crucial in preserving the integrity of email communication. This integration is designed to allow businesses of all sizes to benefit from DMARC’s extensive capabilities.” Ryan Burton, Vice President of Marketplace Vendor Strategy, at Pax8 said: “We’re delighted to welcome EasyDMARC to the Pax8 Marketplace as an enterprise-class DMARC solution provider. This integration gives MSPs the tools they need to meet the growing demand for email security, with simplified deployment, billing, and bundling benefits. With EasyDMARC’s technical capabilities and intelligence, MSPs can deliver robust protection against phishing threats without the technical hassle that often holds businesses back.” About EasyDMARC EasyDMARC is a cloud-native B2B SaaS solution that addresses email security and deliverability problems with just a few clicks. For Managed Service Providers seeking to increase their revenue, EasyDMARC presents an ideal solution. The email authentication platform streamlines domain management, providing capabilities such as organizational control, domain grouping, and access management. Additionally, EasyDMARC offers a comprehensive sales and marketing enablement program designed to boost DMARC sales. All of these features are available for MSPs on a scalable platform with a flexible pay-as-you-go pricing model. For more information on the EasyDMARC, visit: / About Pax8 Pax8 is the technology marketplace of the future, linking partners, vendors, and small to midsized businessesthrough AI-powered insights and comprehensive product support. With a global partner ecosystem of over 38,000 managed service providers, Pax8 empowers SMBs worldwide by providing software and services that unlock their growth potential and enhance their security. Committed to innovating cloud commerce at scale, Pax8 drives customer acquisition and solution consumption across its entire ecosystem. Find out more: / The post EasyDMARC Integrates With Pax8 Marketplace To Simplify Email Security For MSPs appeared first on EasyDMARC. #easydmarc #integrates #with #pax8 #marketplace

EASYDMARC.COM

EasyDMARC Integrates With Pax8 Marketplace To Simplify Email Security For MSPs
Originally published at EasyDMARC Integrates With Pax8 Marketplace To Simplify Email Security For MSPs by Anush Yolyan. The integration will deliver simple, accessible, and streamlined email security for vulnerable inboxes Global, 4 November 2024 – US-based email security firm EasyDMARC has today announced its integration with Pax8 Marketplace, the leading cloud commerce marketplace. As one of the first DMARC solution providers on the Pax8 Marketplace, EasyDMARC is expanding its mission to protect inboxes from the rising threat of phishing attacks with a rigorous, user-friendly DMARC solution. The integration comes as Google highlights the impressive results of recently implemented email authentication measures for bulk senders: a 65% reduction in unauthenticated messages to Gmail users, a 50% increase in bulk senders following best security practices, and 265 billion fewer unauthenticated messages sent in 2024. With email being such a crucial communication channel for businesses, email authentication measures are an essential part of any business’s cybersecurity offering. Key features of the integration include: Centralized billing With centralized billing, customers can now streamline their cloud services under a single pane of glass, simplifying the management and billing of their EasyDMARC solution. This consolidated approach enables partners to reduce administrative complexity and manage all cloud expenses through one interface, providing a seamless billing and support experience. Automated provisioning Through automated provisioning, Pax8’s automation capabilities make deploying DMARC across client accounts quick and hassle-free. By eliminating manual configurations, this integration ensures that customers can implement email security solutions rapidly, allowing them to safeguard client inboxes without delay. Bundled offerings The bundled offerings available through Pax8 allow partners to enhance their service portfolios by combining EasyDMARC with complementary security solutions. By creating all-in-one security packages, partners can offer their clients more robust protection, addressing a broader range of security needs from a single, trusted platform. Gerasim Hovhannisyan, Co-Founder and CEO of EasyDMARC, said: “We’re thrilled to be working with Pax8 to provide MSPs with a streamlined, effective way to deliver top-tier email security to their clients, all within a platform that equips them with everything needed to stay secure. As phishing attacks grow in frequency and sophistication, businesses can no longer afford to overlook the importance of email security. Email authentication is a vital defense against the evolving threat of phishing and is crucial in preserving the integrity of email communication. This integration is designed to allow businesses of all sizes to benefit from DMARC’s extensive capabilities.” Ryan Burton, Vice President of Marketplace Vendor Strategy, at Pax8 said: “We’re delighted to welcome EasyDMARC to the Pax8 Marketplace as an enterprise-class DMARC solution provider. This integration gives MSPs the tools they need to meet the growing demand for email security, with simplified deployment, billing, and bundling benefits. With EasyDMARC’s technical capabilities and intelligence, MSPs can deliver robust protection against phishing threats without the technical hassle that often holds businesses back.” About EasyDMARC EasyDMARC is a cloud-native B2B SaaS solution that addresses email security and deliverability problems with just a few clicks. For Managed Service Providers seeking to increase their revenue, EasyDMARC presents an ideal solution. The email authentication platform streamlines domain management, providing capabilities such as organizational control, domain grouping, and access management. Additionally, EasyDMARC offers a comprehensive sales and marketing enablement program designed to boost DMARC sales. All of these features are available for MSPs on a scalable platform with a flexible pay-as-you-go pricing model. For more information on the EasyDMARC, visit: https://easydmarc.com/ About Pax8 Pax8 is the technology marketplace of the future, linking partners, vendors, and small to midsized businesses (SMBs) through AI-powered insights and comprehensive product support. With a global partner ecosystem of over 38,000 managed service providers, Pax8 empowers SMBs worldwide by providing software and services that unlock their growth potential and enhance their security. Committed to innovating cloud commerce at scale, Pax8 drives customer acquisition and solution consumption across its entire ecosystem. Find out more: https://www.pax8.com/en-us/ The post EasyDMARC Integrates With Pax8 Marketplace To Simplify Email Security For MSPs appeared first on EasyDMARC.

0 Comments 0 Shares

Please log in to like, share and comment!