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2025-06-01 16:00:59 ·

Enigmata’s Multi-Stage and Mix-Training Reinforcement Learning Recipe Drives Breakthrough Performance in LLM Puzzle Reasoning

Large Reasoning Models, trained from LLMs using reinforcement learning, demonstrated great performance in complex reasoning tasks, including mathematics, STEM, and coding. However, existing LRMs face challenges in completing various puzzle tasks that require purely logical reasoning skills, which are easy and obvious for humans. Current methods targeting puzzles focus only on designing benchmarks for evaluation, lacking the training methods and resources for modern LLMs to tackle this challenge. Current puzzle datasets lack diversity and scalability, covering limited puzzle types with little control over generation or difficulty. Moreover, due to the success of the “LLM+RLVR” paradigm, it has become crucial to obtain large, diverse, and challenging sets of verifiable puzzle prompts for training agents.
Reinforcement Learning with Verifiable Rewardshas emerged as a key method for improving models’ reasoning capabilities, removing the need for reward models by directly assigning rewards based on objectively verifiable answers. Puzzles are particularly well-suited for RLVR. However, most prior RLVR research has overlooked the puzzles’ potential for delivering effective reward signals. In puzzle reasoning of LLMs, existing benchmarks evaluate different types of reasoning, including abstract, deductive, and compositional reasoning. Few benchmarks support scalable generation and difficulty control but lack puzzle diversity. Moreover, the improvement of LLMs’ puzzle-solving abilities mainly falls into two categories: tool integration and RLVR.
Researchers from ByteDance Seed, Fudan University, Tsinghua University, Nanjing University, and Shanghai Jiao Tong University have proposed Enigmata, the first comprehensive toolkit designed for improving LLMs with puzzle reasoning skills. It contains 36 tasks across seven categories, each featuring a generator that produces unlimited examples with controllable difficulty and a rule-based verifier for automatic evaluation. The researchers further developed Enigmata-Eval as a rigorous benchmark and created optimized multi-task RLVR strategies. Puzzle data from Enigmata enhances SoTA performance on advanced math and STEM reasoning tasks like AIME, BeyondAIME, and GPQA when trained on larger models like Seed1.5-Thinking. This shows the generalization benefits of Enigmata.

The Enigmata-Data comprises 36 puzzle tasks organized into 7 primary categories, including Crypto, Arithmetic, Logic, Grid, Graph, Search, and Sequential Puzzle, making it the only dataset having multiple task categories with scalability, automatic verification, and public availability. The data construction follows a three-phase pipeline: Tasks Collection and Design, Auto-Generator and Verifier Development, and Sliding Difficulty Control. Moreover, the Enigmata-Eval is developed by systematically sampling from the broader dataset, aiming to extract 50 instances per difficulty level for each task. The final evaluation set contains 4,758 puzzle instances rather than the theoretical maximum of 5,400, due to inherent constraints, where some tasks generate fewer instances per difficulty level.

The proposed model outperforms most public models on Enigmata-Eval with 32B parameters, showing the effectiveness of the dataset and training recipe. The model stands out on the challenging ARC-AGI benchmark, surpassing strong reasoning models such as Gemini 2.5 Pro, o3-mini, and o1. The Qwen2.5-32B-Enigmata shows outstanding performance in structured reasoning categories, outperforming in Crypto, Arithmetic, and Logic tasks, suggesting effective development of rule-based reasoning capabilities. The model shows competitive performance in search tasks that require strategic exploration and planning capabilities. Moreover, Crypto and Arithmetic tasks tend to provide the highest accuracy, while spatial and sequential tasks remain more difficult.
In this paper, researchers introduced Enigmata, a comprehensive suite for equipping LLMs with advanced puzzle reasoning that integrates seamlessly with RL using verifiable rule-based rewards. The trained Enigmata-Model shows superior performance and robust generalization skills through RLVR training. Experiments reveal that when applied to larger models such as Seed1.5-Thinking, synthetic puzzle data brings additional benefits in other domains, including mathematics and STEM reasoning over state-of-the-art models. Enigmata provides a solid foundation for the research community to advance reasoning model development, offering a unified framework that effectively bridges logical puzzle-solving with broader reasoning capabilities in LLMs.

Check out the Paper, GitHub Page and Project Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter.
Sajjad AnsariSajjad Ansari is a final year undergraduate from IIT Kharagpur. As a Tech enthusiast, he delves into the practical applications of AI with a focus on understanding the impact of AI technologies and their real-world implications. He aims to articulate complex AI concepts in a clear and accessible manner.Sajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Multimodal Foundation Models Fall Short on Physical Reasoning: PHYX Benchmark Highlights Key Limitations in Visual and Symbolic IntegrationSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Meta AI Introduces Multi-SpatialMLLM: A Multi-Frame Spatial Understanding with Multi-modal Large Language ModelsSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Can LLMs Really Judge with Reasoning? Microsoft and Tsinghua Researchers Introduce Reward Reasoning Models to Dynamically Scale Test-Time Compute for Better AlignmentSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/NVIDIA AI Introduces AceReason-Nemotron for Advancing Math and Code Reasoning through Reinforcement Learning
#enigmatas #multistage #mixtraining #reinforcement #learning

Enigmata’s Multi-Stage and Mix-Training Reinforcement Learning Recipe Drives Breakthrough Performance in LLM Puzzle Reasoning
Large Reasoning Models, trained from LLMs using reinforcement learning, demonstrated great performance in complex reasoning tasks, including mathematics, STEM, and coding. However, existing LRMs face challenges in completing various puzzle tasks that require purely logical reasoning skills, which are easy and obvious for humans. Current methods targeting puzzles focus only on designing benchmarks for evaluation, lacking the training methods and resources for modern LLMs to tackle this challenge. Current puzzle datasets lack diversity and scalability, covering limited puzzle types with little control over generation or difficulty. Moreover, due to the success of the “LLM+RLVR” paradigm, it has become crucial to obtain large, diverse, and challenging sets of verifiable puzzle prompts for training agents. Reinforcement Learning with Verifiable Rewardshas emerged as a key method for improving models’ reasoning capabilities, removing the need for reward models by directly assigning rewards based on objectively verifiable answers. Puzzles are particularly well-suited for RLVR. However, most prior RLVR research has overlooked the puzzles’ potential for delivering effective reward signals. In puzzle reasoning of LLMs, existing benchmarks evaluate different types of reasoning, including abstract, deductive, and compositional reasoning. Few benchmarks support scalable generation and difficulty control but lack puzzle diversity. Moreover, the improvement of LLMs’ puzzle-solving abilities mainly falls into two categories: tool integration and RLVR. Researchers from ByteDance Seed, Fudan University, Tsinghua University, Nanjing University, and Shanghai Jiao Tong University have proposed Enigmata, the first comprehensive toolkit designed for improving LLMs with puzzle reasoning skills. It contains 36 tasks across seven categories, each featuring a generator that produces unlimited examples with controllable difficulty and a rule-based verifier for automatic evaluation. The researchers further developed Enigmata-Eval as a rigorous benchmark and created optimized multi-task RLVR strategies. Puzzle data from Enigmata enhances SoTA performance on advanced math and STEM reasoning tasks like AIME, BeyondAIME, and GPQA when trained on larger models like Seed1.5-Thinking. This shows the generalization benefits of Enigmata. The Enigmata-Data comprises 36 puzzle tasks organized into 7 primary categories, including Crypto, Arithmetic, Logic, Grid, Graph, Search, and Sequential Puzzle, making it the only dataset having multiple task categories with scalability, automatic verification, and public availability. The data construction follows a three-phase pipeline: Tasks Collection and Design, Auto-Generator and Verifier Development, and Sliding Difficulty Control. Moreover, the Enigmata-Eval is developed by systematically sampling from the broader dataset, aiming to extract 50 instances per difficulty level for each task. The final evaluation set contains 4,758 puzzle instances rather than the theoretical maximum of 5,400, due to inherent constraints, where some tasks generate fewer instances per difficulty level. The proposed model outperforms most public models on Enigmata-Eval with 32B parameters, showing the effectiveness of the dataset and training recipe. The model stands out on the challenging ARC-AGI benchmark, surpassing strong reasoning models such as Gemini 2.5 Pro, o3-mini, and o1. The Qwen2.5-32B-Enigmata shows outstanding performance in structured reasoning categories, outperforming in Crypto, Arithmetic, and Logic tasks, suggesting effective development of rule-based reasoning capabilities. The model shows competitive performance in search tasks that require strategic exploration and planning capabilities. Moreover, Crypto and Arithmetic tasks tend to provide the highest accuracy, while spatial and sequential tasks remain more difficult. In this paper, researchers introduced Enigmata, a comprehensive suite for equipping LLMs with advanced puzzle reasoning that integrates seamlessly with RL using verifiable rule-based rewards. The trained Enigmata-Model shows superior performance and robust generalization skills through RLVR training. Experiments reveal that when applied to larger models such as Seed1.5-Thinking, synthetic puzzle data brings additional benefits in other domains, including mathematics and STEM reasoning over state-of-the-art models. Enigmata provides a solid foundation for the research community to advance reasoning model development, offering a unified framework that effectively bridges logical puzzle-solving with broader reasoning capabilities in LLMs. Check out the Paper, GitHub Page and Project Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. Sajjad AnsariSajjad Ansari is a final year undergraduate from IIT Kharagpur. As a Tech enthusiast, he delves into the practical applications of AI with a focus on understanding the impact of AI technologies and their real-world implications. He aims to articulate complex AI concepts in a clear and accessible manner.Sajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Multimodal Foundation Models Fall Short on Physical Reasoning: PHYX Benchmark Highlights Key Limitations in Visual and Symbolic IntegrationSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Meta AI Introduces Multi-SpatialMLLM: A Multi-Frame Spatial Understanding with Multi-modal Large Language ModelsSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Can LLMs Really Judge with Reasoning? Microsoft and Tsinghua Researchers Introduce Reward Reasoning Models to Dynamically Scale Test-Time Compute for Better AlignmentSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/NVIDIA AI Introduces AceReason-Nemotron for Advancing Math and Code Reasoning through Reinforcement Learning #enigmatas #multistage #mixtraining #reinforcement #learning

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Enigmata’s Multi-Stage and Mix-Training Reinforcement Learning Recipe Drives Breakthrough Performance in LLM Puzzle Reasoning

Large Reasoning Models (LRMs), trained from LLMs using reinforcement learning (RL), demonstrated great performance in complex reasoning tasks, including mathematics, STEM, and coding. However, existing LRMs face challenges in completing various puzzle tasks that require purely logical reasoning skills, which are easy and obvious for humans. Current methods targeting puzzles focus only on designing benchmarks for evaluation, lacking the training methods and resources for modern LLMs to tackle this challenge. Current puzzle datasets lack diversity and scalability, covering limited puzzle types with little control over generation or difficulty. Moreover, due to the success of the “LLM+RLVR” paradigm, it has become crucial to obtain large, diverse, and challenging sets of verifiable puzzle prompts for training agents. Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a key method for improving models’ reasoning capabilities, removing the need for reward models by directly assigning rewards based on objectively verifiable answers. Puzzles are particularly well-suited for RLVR. However, most prior RLVR research has overlooked the puzzles’ potential for delivering effective reward signals. In puzzle reasoning of LLMs, existing benchmarks evaluate different types of reasoning, including abstract, deductive, and compositional reasoning. Few benchmarks support scalable generation and difficulty control but lack puzzle diversity. Moreover, the improvement of LLMs’ puzzle-solving abilities mainly falls into two categories: tool integration and RLVR. Researchers from ByteDance Seed, Fudan University, Tsinghua University, Nanjing University, and Shanghai Jiao Tong University have proposed Enigmata, the first comprehensive toolkit designed for improving LLMs with puzzle reasoning skills. It contains 36 tasks across seven categories, each featuring a generator that produces unlimited examples with controllable difficulty and a rule-based verifier for automatic evaluation. The researchers further developed Enigmata-Eval as a rigorous benchmark and created optimized multi-task RLVR strategies. Puzzle data from Enigmata enhances SoTA performance on advanced math and STEM reasoning tasks like AIME, BeyondAIME, and GPQA when trained on larger models like Seed1.5-Thinking. This shows the generalization benefits of Enigmata. The Enigmata-Data comprises 36 puzzle tasks organized into 7 primary categories, including Crypto, Arithmetic, Logic, Grid, Graph, Search, and Sequential Puzzle, making it the only dataset having multiple task categories with scalability, automatic verification, and public availability. The data construction follows a three-phase pipeline: Tasks Collection and Design, Auto-Generator and Verifier Development, and Sliding Difficulty Control. Moreover, the Enigmata-Eval is developed by systematically sampling from the broader dataset, aiming to extract 50 instances per difficulty level for each task. The final evaluation set contains 4,758 puzzle instances rather than the theoretical maximum of 5,400, due to inherent constraints, where some tasks generate fewer instances per difficulty level. The proposed model outperforms most public models on Enigmata-Eval with 32B parameters, showing the effectiveness of the dataset and training recipe. The model stands out on the challenging ARC-AGI benchmark, surpassing strong reasoning models such as Gemini 2.5 Pro, o3-mini, and o1. The Qwen2.5-32B-Enigmata shows outstanding performance in structured reasoning categories, outperforming in Crypto, Arithmetic, and Logic tasks, suggesting effective development of rule-based reasoning capabilities. The model shows competitive performance in search tasks that require strategic exploration and planning capabilities. Moreover, Crypto and Arithmetic tasks tend to provide the highest accuracy, while spatial and sequential tasks remain more difficult. In this paper, researchers introduced Enigmata, a comprehensive suite for equipping LLMs with advanced puzzle reasoning that integrates seamlessly with RL using verifiable rule-based rewards. The trained Enigmata-Model shows superior performance and robust generalization skills through RLVR training. Experiments reveal that when applied to larger models such as Seed1.5-Thinking (20B/200B parameters), synthetic puzzle data brings additional benefits in other domains, including mathematics and STEM reasoning over state-of-the-art models. Enigmata provides a solid foundation for the research community to advance reasoning model development, offering a unified framework that effectively bridges logical puzzle-solving with broader reasoning capabilities in LLMs. Check out the Paper, GitHub Page and Project Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. Sajjad AnsariSajjad Ansari is a final year undergraduate from IIT Kharagpur. As a Tech enthusiast, he delves into the practical applications of AI with a focus on understanding the impact of AI technologies and their real-world implications. He aims to articulate complex AI concepts in a clear and accessible manner.Sajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Multimodal Foundation Models Fall Short on Physical Reasoning: PHYX Benchmark Highlights Key Limitations in Visual and Symbolic IntegrationSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Meta AI Introduces Multi-SpatialMLLM: A Multi-Frame Spatial Understanding with Multi-modal Large Language ModelsSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Can LLMs Really Judge with Reasoning? Microsoft and Tsinghua Researchers Introduce Reward Reasoning Models to Dynamically Scale Test-Time Compute for Better AlignmentSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/NVIDIA AI Introduces AceReason-Nemotron for Advancing Math and Code Reasoning through Reinforcement Learning

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Towards Data Science shared a link

2025-05-31 23:45:26 ·

LLM Optimization: LoRA and QLoRA

Scalable fine-tuning techniques for large language models
The post LLM Optimization: LoRA and QLoRA appeared first on Towards Data Science.
#llm #optimization #lora #andqlora

LLM Optimization: LoRA and QLoRA
Scalable fine-tuning techniques for large language models The post LLM Optimization: LoRA and QLoRA appeared first on Towards Data Science. #llm #optimization #lora #andqlora

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LLM Optimization: LoRA and QLoRA

Scalable fine-tuning techniques for large language models The post LLM Optimization: LoRA and QLoRA appeared first on Towards Data Science.

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2025-05-31 22:53:51 ·

Meet NovelSeek: A Unified Multi-Agent Framework for Autonomous Scientific Research from Hypothesis Generation to Experimental Validation

Scientific research across fields like chemistry, biology, and artificial intelligence has long relied on human experts to explore knowledge, generate ideas, design experiments, and refine results. Yet, as problems grow more complex and data-intensive, discovery slows. While AI tools, such as language models and robotics, can handle specific tasks, like literature searches or code analysis, they rarely encompass the entire research cycle. Bridging the gap between idea generation and experimental validation remains a key challenge. For AI to autonomously advance science, it must propose hypotheses, design and execute experiments, analyze outcomes, and refine approaches in an iterative loop. Without this integration, AI risks producing disconnected ideas that depend on human supervision for validation.
Before the introduction of a unified system, researchers relied on separate tools for each stage of the process. Large language models could help find relevant scientific papers, but they didn’t directly feed into experiment design or result analysis. Robotics can assist in automating physical experiments, and coding libraries like PyTorch can help build models; however, these tools operate independently of each other. There was no single system capable of handling the entire process, from forming ideas to verifying them through experiments. This led to bottlenecks, where researchers had to connect the dots manually, slowing progress and leaving room for errors or missed opportunities. The need for an integrated system that could handle the entire research cycle became clear.
Researchers from the NovelSeek Team at the Shanghai Artificial Intelligence Laboratory developed NovelSeek, an AI system designed to run the entire scientific discovery process autonomously. NovelSeek comprises four main modules that work in tandem: a system that generates and refines research ideas, a feedback loop where human experts can interact with and refine these ideas, a method for translating ideas into code and experiment plans, and a process for conducting multiple rounds of experiments. What makes NovelSeek stand out is its versatility; it works across 12 scientific research tasks, including predicting chemical reaction yields, understanding molecular dynamics, forecasting time-series data, and handling functions like 2D semantic segmentation and 3D object classification. The team designed NovelSeek to minimize human involvement, expedite discoveries, and deliver consistent, high-quality results.

The system behind NovelSeek involves multiple specialized agents, each focused on a specific part of the research workflow. The “Survey Agent” helps the system understand the problem by searching scientific papers and identifying relevant information based on keywords and task definitions. It adapts its search strategy by first doing a broad survey of papers, then going deeper by analyzing full-text documents for detailed insights. This ensures that the system captures both general trends and specific technical knowledge. The “Code Review Agent” examines existing codebases, whether user-uploaded or sourced from public repositories like GitHub, to understand how current methods work and identify areas for improvement. It checks how code is structured, looks for errors, and creates summaries that help the system build on past work. The “Idea Innovation Agent” generates creative research ideas, pushing the system to explore different approaches and refine them by comparing them to related studies and previous results. The system even includes a “Planning and Execution Agent” that turns ideas into detailed experiments, handles errors during the testing process, and ensures smooth execution of multi-step research plans.

NovelSeek delivered impressive results across various tasks. In chemical reaction yield prediction, NovelSeek improved performance from a baseline of 24.2%to 34.8%in just 12 hours, progress that human researchers typically need months to achieve. In enhancer activity prediction, a key task in biology, NovelSeek raised the Pearson correlation coefficient from 0.65 to 0.79 within 4 hours. For 2D semantic segmentation, a task used in computer vision, precision improved from 78.8% to 81.0% in just 30 hours. These performance boosts, achieved in a fraction of the time typically needed, highlight the system’s efficiency. NovelSeek also successfully managed large, complex codebases with multiple files, demonstrating its ability to handle research tasks at a project level, not just in small, isolated tests. The team has made the code open-source, allowing others to use, test, and contribute to its improvement.

Several Key Takeaways from the Research on NovelSeek include:

NovelSeek supports 12 research tasks, including chemical reaction prediction, molecular dynamics, and 3D object classification.
Reaction yield prediction accuracy improved from 24.2% to 34.8% in 12 hours.
Enhancer activity prediction performance increased from 0.65 to 0.79 in 4 hours.
2D semantic segmentation precision improved from 78.8% to 81.0% in 30 hours.
NovelSeek includes agents for literature search, code analysis, idea generation, and experiment execution.
The system is open-source, enabling reproducibility and collaboration across scientific fields.

In conclusion, NovelSeek demonstrates how combining AI tools into a single system can accelerate scientific discovery and reduce its dependence on human effort. It ties together the key steps, generating ideas, turning them into methods, and testing them through experiments, into one streamlined process. What once took researchers months or years can now be done in days or even hours. By linking every stage of research into a continuous loop, NovelSeek helps teams move from rough ideas to real-world results more quickly. This system highlights the power of AI not just to assist, but to drive scientific research in a way that could reshape how discoveries are made across many fields.

Check out the Paper and GitHub Page . All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter.
NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces ARM and Ada-GRPO: Adaptive Reasoning Models for Efficient and Scalable Problem-SolvingNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces WEB-SHEPHERD: A Process Reward Model for Web Agents with 40K Dataset and 10× Cost EfficiencyNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces MMaDA: A Unified Multimodal Diffusion Model for Textual Reasoning, Visual Understanding, and Image GenerationNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces Differentiable MCMC Layers: A New AI Framework for Learning with Inexact Combinatorial Solvers in Neural Networks
#meet #novelseek #unified #multiagent #framework

Meet NovelSeek: A Unified Multi-Agent Framework for Autonomous Scientific Research from Hypothesis Generation to Experimental Validation
Scientific research across fields like chemistry, biology, and artificial intelligence has long relied on human experts to explore knowledge, generate ideas, design experiments, and refine results. Yet, as problems grow more complex and data-intensive, discovery slows. While AI tools, such as language models and robotics, can handle specific tasks, like literature searches or code analysis, they rarely encompass the entire research cycle. Bridging the gap between idea generation and experimental validation remains a key challenge. For AI to autonomously advance science, it must propose hypotheses, design and execute experiments, analyze outcomes, and refine approaches in an iterative loop. Without this integration, AI risks producing disconnected ideas that depend on human supervision for validation. Before the introduction of a unified system, researchers relied on separate tools for each stage of the process. Large language models could help find relevant scientific papers, but they didn’t directly feed into experiment design or result analysis. Robotics can assist in automating physical experiments, and coding libraries like PyTorch can help build models; however, these tools operate independently of each other. There was no single system capable of handling the entire process, from forming ideas to verifying them through experiments. This led to bottlenecks, where researchers had to connect the dots manually, slowing progress and leaving room for errors or missed opportunities. The need for an integrated system that could handle the entire research cycle became clear. Researchers from the NovelSeek Team at the Shanghai Artificial Intelligence Laboratory developed NovelSeek, an AI system designed to run the entire scientific discovery process autonomously. NovelSeek comprises four main modules that work in tandem: a system that generates and refines research ideas, a feedback loop where human experts can interact with and refine these ideas, a method for translating ideas into code and experiment plans, and a process for conducting multiple rounds of experiments. What makes NovelSeek stand out is its versatility; it works across 12 scientific research tasks, including predicting chemical reaction yields, understanding molecular dynamics, forecasting time-series data, and handling functions like 2D semantic segmentation and 3D object classification. The team designed NovelSeek to minimize human involvement, expedite discoveries, and deliver consistent, high-quality results. The system behind NovelSeek involves multiple specialized agents, each focused on a specific part of the research workflow. The “Survey Agent” helps the system understand the problem by searching scientific papers and identifying relevant information based on keywords and task definitions. It adapts its search strategy by first doing a broad survey of papers, then going deeper by analyzing full-text documents for detailed insights. This ensures that the system captures both general trends and specific technical knowledge. The “Code Review Agent” examines existing codebases, whether user-uploaded or sourced from public repositories like GitHub, to understand how current methods work and identify areas for improvement. It checks how code is structured, looks for errors, and creates summaries that help the system build on past work. The “Idea Innovation Agent” generates creative research ideas, pushing the system to explore different approaches and refine them by comparing them to related studies and previous results. The system even includes a “Planning and Execution Agent” that turns ideas into detailed experiments, handles errors during the testing process, and ensures smooth execution of multi-step research plans. NovelSeek delivered impressive results across various tasks. In chemical reaction yield prediction, NovelSeek improved performance from a baseline of 24.2%to 34.8%in just 12 hours, progress that human researchers typically need months to achieve. In enhancer activity prediction, a key task in biology, NovelSeek raised the Pearson correlation coefficient from 0.65 to 0.79 within 4 hours. For 2D semantic segmentation, a task used in computer vision, precision improved from 78.8% to 81.0% in just 30 hours. These performance boosts, achieved in a fraction of the time typically needed, highlight the system’s efficiency. NovelSeek also successfully managed large, complex codebases with multiple files, demonstrating its ability to handle research tasks at a project level, not just in small, isolated tests. The team has made the code open-source, allowing others to use, test, and contribute to its improvement. Several Key Takeaways from the Research on NovelSeek include: NovelSeek supports 12 research tasks, including chemical reaction prediction, molecular dynamics, and 3D object classification. Reaction yield prediction accuracy improved from 24.2% to 34.8% in 12 hours. Enhancer activity prediction performance increased from 0.65 to 0.79 in 4 hours. 2D semantic segmentation precision improved from 78.8% to 81.0% in 30 hours. NovelSeek includes agents for literature search, code analysis, idea generation, and experiment execution. The system is open-source, enabling reproducibility and collaboration across scientific fields. In conclusion, NovelSeek demonstrates how combining AI tools into a single system can accelerate scientific discovery and reduce its dependence on human effort. It ties together the key steps, generating ideas, turning them into methods, and testing them through experiments, into one streamlined process. What once took researchers months or years can now be done in days or even hours. By linking every stage of research into a continuous loop, NovelSeek helps teams move from rough ideas to real-world results more quickly. This system highlights the power of AI not just to assist, but to drive scientific research in a way that could reshape how discoveries are made across many fields. Check out the Paper and GitHub Page . All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces ARM and Ada-GRPO: Adaptive Reasoning Models for Efficient and Scalable Problem-SolvingNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces WEB-SHEPHERD: A Process Reward Model for Web Agents with 40K Dataset and 10× Cost EfficiencyNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces MMaDA: A Unified Multimodal Diffusion Model for Textual Reasoning, Visual Understanding, and Image GenerationNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces Differentiable MCMC Layers: A New AI Framework for Learning with Inexact Combinatorial Solvers in Neural Networks #meet #novelseek #unified #multiagent #framework

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Meet NovelSeek: A Unified Multi-Agent Framework for Autonomous Scientific Research from Hypothesis Generation to Experimental Validation

Scientific research across fields like chemistry, biology, and artificial intelligence has long relied on human experts to explore knowledge, generate ideas, design experiments, and refine results. Yet, as problems grow more complex and data-intensive, discovery slows. While AI tools, such as language models and robotics, can handle specific tasks, like literature searches or code analysis, they rarely encompass the entire research cycle. Bridging the gap between idea generation and experimental validation remains a key challenge. For AI to autonomously advance science, it must propose hypotheses, design and execute experiments, analyze outcomes, and refine approaches in an iterative loop. Without this integration, AI risks producing disconnected ideas that depend on human supervision for validation. Before the introduction of a unified system, researchers relied on separate tools for each stage of the process. Large language models could help find relevant scientific papers, but they didn’t directly feed into experiment design or result analysis. Robotics can assist in automating physical experiments, and coding libraries like PyTorch can help build models; however, these tools operate independently of each other. There was no single system capable of handling the entire process, from forming ideas to verifying them through experiments. This led to bottlenecks, where researchers had to connect the dots manually, slowing progress and leaving room for errors or missed opportunities. The need for an integrated system that could handle the entire research cycle became clear. Researchers from the NovelSeek Team at the Shanghai Artificial Intelligence Laboratory developed NovelSeek, an AI system designed to run the entire scientific discovery process autonomously. NovelSeek comprises four main modules that work in tandem: a system that generates and refines research ideas, a feedback loop where human experts can interact with and refine these ideas, a method for translating ideas into code and experiment plans, and a process for conducting multiple rounds of experiments. What makes NovelSeek stand out is its versatility; it works across 12 scientific research tasks, including predicting chemical reaction yields, understanding molecular dynamics, forecasting time-series data, and handling functions like 2D semantic segmentation and 3D object classification. The team designed NovelSeek to minimize human involvement, expedite discoveries, and deliver consistent, high-quality results. The system behind NovelSeek involves multiple specialized agents, each focused on a specific part of the research workflow. The “Survey Agent” helps the system understand the problem by searching scientific papers and identifying relevant information based on keywords and task definitions. It adapts its search strategy by first doing a broad survey of papers, then going deeper by analyzing full-text documents for detailed insights. This ensures that the system captures both general trends and specific technical knowledge. The “Code Review Agent” examines existing codebases, whether user-uploaded or sourced from public repositories like GitHub, to understand how current methods work and identify areas for improvement. It checks how code is structured, looks for errors, and creates summaries that help the system build on past work. The “Idea Innovation Agent” generates creative research ideas, pushing the system to explore different approaches and refine them by comparing them to related studies and previous results. The system even includes a “Planning and Execution Agent” that turns ideas into detailed experiments, handles errors during the testing process, and ensures smooth execution of multi-step research plans. NovelSeek delivered impressive results across various tasks. In chemical reaction yield prediction, NovelSeek improved performance from a baseline of 24.2% (with a variation of ±4.2) to 34.8% (with a much smaller variation of ±1.1) in just 12 hours, progress that human researchers typically need months to achieve. In enhancer activity prediction, a key task in biology, NovelSeek raised the Pearson correlation coefficient from 0.65 to 0.79 within 4 hours. For 2D semantic segmentation, a task used in computer vision, precision improved from 78.8% to 81.0% in just 30 hours. These performance boosts, achieved in a fraction of the time typically needed, highlight the system’s efficiency. NovelSeek also successfully managed large, complex codebases with multiple files, demonstrating its ability to handle research tasks at a project level, not just in small, isolated tests. The team has made the code open-source, allowing others to use, test, and contribute to its improvement. Several Key Takeaways from the Research on NovelSeek include: NovelSeek supports 12 research tasks, including chemical reaction prediction, molecular dynamics, and 3D object classification. Reaction yield prediction accuracy improved from 24.2% to 34.8% in 12 hours. Enhancer activity prediction performance increased from 0.65 to 0.79 in 4 hours. 2D semantic segmentation precision improved from 78.8% to 81.0% in 30 hours. NovelSeek includes agents for literature search, code analysis, idea generation, and experiment execution. The system is open-source, enabling reproducibility and collaboration across scientific fields. In conclusion, NovelSeek demonstrates how combining AI tools into a single system can accelerate scientific discovery and reduce its dependence on human effort. It ties together the key steps, generating ideas, turning them into methods, and testing them through experiments, into one streamlined process. What once took researchers months or years can now be done in days or even hours. By linking every stage of research into a continuous loop, NovelSeek helps teams move from rough ideas to real-world results more quickly. This system highlights the power of AI not just to assist, but to drive scientific research in a way that could reshape how discoveries are made across many fields. Check out the Paper and GitHub Page . All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces ARM and Ada-GRPO: Adaptive Reasoning Models for Efficient and Scalable Problem-SolvingNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces WEB-SHEPHERD: A Process Reward Model for Web Agents with 40K Dataset and 10× Cost EfficiencyNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces MMaDA: A Unified Multimodal Diffusion Model for Textual Reasoning, Visual Understanding, and Image GenerationNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces Differentiable MCMC Layers: A New AI Framework for Learning with Inexact Combinatorial Solvers in Neural Networks

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2025-05-31 22:53:43 ·

Cozy Games Free & Open-Source Godot Add-Ons

Cozy Games Free & Open-Source Godot Add-Ons / News, Resources / May 31, 2025 / Add-On, Godot

The folks over at Cozy Cube Games have provided a GitHub repository that offers a fantastic collection of tools to enhance your Godot game development workflow. Here’s a look at 14 of the add-ons you can find there, all of which are available under the MIT open-source license and implemented using 100% GDScript.
The repository consists of the following free add-ons for the Godot game engine:
area_lights: Enhances lighting capabilities within your scenes.
control_proxies:
doodle_texture: Create textures directly in Godot by doodling or sketching, perfect for prototyping and “sticky notes”.
editor_relays: This add-on allows you to manage the Godot Editor from your application at run-time.
gizmo_presets: Gain precise control over the visibility of Gizmos in Godot. Show/hide them all, or create and toggle your own custom presets.
light_shafts: Add breathtaking volumetric light scattering effects, often known as “god rays,” to your scenes.
lines_and_trails_3d: Easily create and manage dynamic 3D lines and trail effects for projectiles, movement paths, and more.
manual_multimesh: Optimize your scenes by gaining more direct control over MultiMeshInstances, perfect for rendering many similar meshes efficiently.
nine_patch_mesh: Create scalable 3D nine-patch meshes, ideal for flexible UI elements or environment pieces with repeating borders and centers.
parallel_scene_views: Boost your productivity by viewing and interacting with multiple scene views simultaneously within the editor.
preview_2d: Seamlessly integrate 2D and 3D workflows by adding a button to preview your 2D viewport directly from a 3D scene.
procedural_texture_baker: Generate and bake unique procedural textures directly within Godot, saving time and adding variety.
tile_path_3d: Effortlessly instance 3D objects along a spline path. Perfect for creating paths, roads, fences, and other repeating environmental details.
transform_clusters: Once again, not really sure the use case on this one
Key Links
Cozy Cube Games Godot Add-Ons GitHub Repository
Cozy Cube Games Homepage
You can learn more about the Cozy Cube Games add-ons for the Godot game engine as well as see many of them in action in the video below.
#cozy #games #free #ampamp #opensource

Cozy Games Free & Open-Source Godot Add-Ons
Cozy Games Free & Open-Source Godot Add-Ons / News, Resources / May 31, 2025 / Add-On, Godot The folks over at Cozy Cube Games have provided a GitHub repository that offers a fantastic collection of tools to enhance your Godot game development workflow. Here’s a look at 14 of the add-ons you can find there, all of which are available under the MIT open-source license and implemented using 100% GDScript. The repository consists of the following free add-ons for the Godot game engine: area_lights: Enhances lighting capabilities within your scenes. control_proxies: doodle_texture: Create textures directly in Godot by doodling or sketching, perfect for prototyping and “sticky notes”. editor_relays: This add-on allows you to manage the Godot Editor from your application at run-time. gizmo_presets: Gain precise control over the visibility of Gizmos in Godot. Show/hide them all, or create and toggle your own custom presets. light_shafts: Add breathtaking volumetric light scattering effects, often known as “god rays,” to your scenes. lines_and_trails_3d: Easily create and manage dynamic 3D lines and trail effects for projectiles, movement paths, and more. manual_multimesh: Optimize your scenes by gaining more direct control over MultiMeshInstances, perfect for rendering many similar meshes efficiently. nine_patch_mesh: Create scalable 3D nine-patch meshes, ideal for flexible UI elements or environment pieces with repeating borders and centers. parallel_scene_views: Boost your productivity by viewing and interacting with multiple scene views simultaneously within the editor. preview_2d: Seamlessly integrate 2D and 3D workflows by adding a button to preview your 2D viewport directly from a 3D scene. procedural_texture_baker: Generate and bake unique procedural textures directly within Godot, saving time and adding variety. tile_path_3d: Effortlessly instance 3D objects along a spline path. Perfect for creating paths, roads, fences, and other repeating environmental details. transform_clusters: Once again, not really sure the use case on this one Key Links Cozy Cube Games Godot Add-Ons GitHub Repository Cozy Cube Games Homepage You can learn more about the Cozy Cube Games add-ons for the Godot game engine as well as see many of them in action in the video below. #cozy #games #free #ampamp #opensource

GAMEFROMSCRATCH.COM

Cozy Games Free & Open-Source Godot Add-Ons

Cozy Games Free & Open-Source Godot Add-Ons / News, Resources / May 31, 2025 / Add-On, Godot The folks over at Cozy Cube Games have provided a GitHub repository that offers a fantastic collection of tools to enhance your Godot game development workflow. Here’s a look at 14 of the add-ons you can find there, all of which are available under the MIT open-source license and implemented using 100% GDScript. The repository consists of the following free add-ons for the Godot game engine: area_lights: Enhances lighting capabilities within your scenes. control_proxies: doodle_texture: Create textures directly in Godot by doodling or sketching, perfect for prototyping and “sticky notes”. editor_relays: This add-on allows you to manage the Godot Editor from your application at run-time. gizmo_presets: Gain precise control over the visibility of Gizmos in Godot. Show/hide them all, or create and toggle your own custom presets. light_shafts: Add breathtaking volumetric light scattering effects, often known as “god rays,” to your scenes. lines_and_trails_3d: Easily create and manage dynamic 3D lines and trail effects for projectiles, movement paths, and more. manual_multimesh: Optimize your scenes by gaining more direct control over MultiMeshInstances, perfect for rendering many similar meshes efficiently. nine_patch_mesh: Create scalable 3D nine-patch meshes, ideal for flexible UI elements or environment pieces with repeating borders and centers. parallel_scene_views: Boost your productivity by viewing and interacting with multiple scene views simultaneously within the editor. preview_2d: Seamlessly integrate 2D and 3D workflows by adding a button to preview your 2D viewport directly from a 3D scene. procedural_texture_baker: Generate and bake unique procedural textures directly within Godot, saving time and adding variety. tile_path_3d: Effortlessly instance 3D objects along a spline path. Perfect for creating paths, roads, fences, and other repeating environmental details. transform_clusters: Once again, not really sure the use case on this one Key Links Cozy Cube Games Godot Add-Ons GitHub Repository Cozy Cube Games Homepage You can learn more about the Cozy Cube Games add-ons for the Godot game engine as well as see many of them in action in the video below.

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2025-05-31 13:10:25 ·

Engineering Lead, Data Platform at Epic Games

Engineering Lead, Data PlatformEpic GamesCary, North Carolina, United States11 hours agoApplyWHAT MAKES US EPIC?At the core of Epic’s success are talented, passionate people. Epic prides itself on creating a collaborative, welcoming, and creative environment. Whether it’s building award-winning games or crafting engine technology that enables others to make visually stunning interactive experiences, we’re always innovating.Being Epic means being a part of a team that continually strives to do right by our community and users. We’re constantly innovating to raise the bar of engine and game development.DATA ENGINEERINGWhat We DoOur mission is to provide a world-class platform that empowers the business to leverage data that will enhance, monitor, and support our products. We are responsible for data ingestion systems, processing pipelines, and various data stores all operating in the cloud. We operate at a petabyte scale, and support near real-time use cases as well as more traditional batch approaches.What You'll DoEpic Games is seeking a Senior Engineering Lead to guide the Data Services team, which builds and maintains the core services behind our data platform. This team handles telemetry collection, data schematization, stream routing, data lake integration, and real-time analytics, bridging platform, data, and backend engineering. In this role, you’ll lead team growth and mentorship, drive alignment on technical strategy, and collaborate cross-functionally to scale our data infrastructure.In this role, you willLead, mentor, and grow a team of senior and principal engineersFoster an inclusive, collaborative, and feedback-driven engineering cultureDrive continuous improvement in the team’s processes, delivery, and impactCollaborate with stakeholders in engineering, data science, and analytics to shape and communicate the team’s vision, strategy, and roadmapBridge strategic vision and tactical execution by breaking down long-term goals into achievable, well-scoped iterations that deliver continuous valueEnsure high standards in system architecture, code quality, and operational excellenceWhat we're looking for3+ years of engineering management experience leading high-performing teams in data platform or infrastructure environmentsProven track record navigating complex systems, ambiguous requirements, and high-pressure situations with confidence and clarityDeep experience in architecting, building, and operating scalable, distributed data platformsStrong technical leadership skills, including the ability to review architecture/design documents and provide actionable feedback on code and systemsAbility to engage deeply in technical discussions, review architecture and design documents, evaluate pull requests, and step in during high-priority incidents when needed — even if hands-on coding isn’t a part of the day-to-dayHands-on experience with distributed event streaming systems like Apache KafkaFamiliarity with OLAP databases such as Apache Pinot or ClickHouseProficient in modern data lake and warehouse tools such as S3, Databricks, or SnowflakeExperience with distributed data processing engines like Apache Flink or Apache SparkStrong foundation in the JVM ecosystem, container orchestration with Kubernetes, and cloud platforms, especially AWSEPIC JOB + EPIC BENEFITS = EPIC LIFEOur intent is to cover all things that are medically necessary and improve the quality of life. We pay 100% of the premiums for both you and your dependents. Our coverage includes Medical, Dental, a Vision HRA, Long Term Disability, Life Insurance & a 401k with competitive match. We also offer a robust mental well-being program through Modern Health, which provides free therapy and coaching for employees & dependents. Throughout the year we celebrate our employees with events and company-wide paid breaks. We offer unlimited PTO and sick time and recognize individuals for 7 years of employment with a paid sabbatical.ABOUT USEpic Games spans across 25 countries with 46 studios and 4,500+ employees globally. For over 25 years, we've been making award-winning games and engine technology that empowers others to make visually stunning games and 3D content that bring environments to life like never before. Epic's award-winning Unreal Engine technology not only provides game developers the ability to build high-fidelity, interactive experiences for PC, console, mobile, and VR, it is also a tool being embraced by content creators across a variety of industries such as media and entertainment, automotive, and architectural design. As we continue to build our Engine technology and develop remarkable games, we strive to build teams of world-class talent.Like what you hear? Come be a part of something Epic!Epic Games deeply values diverse teams and an inclusive work culture, and we are proud to be an Equal Opportunity employer. Learn more about our Equal Employment OpportunityPolicy here.Note to Recruitment Agencies: Epic does not accept any unsolicited resumes or approaches from any unauthorized third party. We will not pay any fees to any unauthorized third party. Further details on these matters can be found here.
Create Your Profile — Game companies can contact you with their relevant job openings.
Apply
#engineering #lead #data #platform #epic

Engineering Lead, Data Platform at Epic Games
Engineering Lead, Data PlatformEpic GamesCary, North Carolina, United States11 hours agoApplyWHAT MAKES US EPIC?At the core of Epic’s success are talented, passionate people. Epic prides itself on creating a collaborative, welcoming, and creative environment. Whether it’s building award-winning games or crafting engine technology that enables others to make visually stunning interactive experiences, we’re always innovating.Being Epic means being a part of a team that continually strives to do right by our community and users. We’re constantly innovating to raise the bar of engine and game development.DATA ENGINEERINGWhat We DoOur mission is to provide a world-class platform that empowers the business to leverage data that will enhance, monitor, and support our products. We are responsible for data ingestion systems, processing pipelines, and various data stores all operating in the cloud. We operate at a petabyte scale, and support near real-time use cases as well as more traditional batch approaches.What You'll DoEpic Games is seeking a Senior Engineering Lead to guide the Data Services team, which builds and maintains the core services behind our data platform. This team handles telemetry collection, data schematization, stream routing, data lake integration, and real-time analytics, bridging platform, data, and backend engineering. In this role, you’ll lead team growth and mentorship, drive alignment on technical strategy, and collaborate cross-functionally to scale our data infrastructure.In this role, you willLead, mentor, and grow a team of senior and principal engineersFoster an inclusive, collaborative, and feedback-driven engineering cultureDrive continuous improvement in the team’s processes, delivery, and impactCollaborate with stakeholders in engineering, data science, and analytics to shape and communicate the team’s vision, strategy, and roadmapBridge strategic vision and tactical execution by breaking down long-term goals into achievable, well-scoped iterations that deliver continuous valueEnsure high standards in system architecture, code quality, and operational excellenceWhat we're looking for3+ years of engineering management experience leading high-performing teams in data platform or infrastructure environmentsProven track record navigating complex systems, ambiguous requirements, and high-pressure situations with confidence and clarityDeep experience in architecting, building, and operating scalable, distributed data platformsStrong technical leadership skills, including the ability to review architecture/design documents and provide actionable feedback on code and systemsAbility to engage deeply in technical discussions, review architecture and design documents, evaluate pull requests, and step in during high-priority incidents when needed — even if hands-on coding isn’t a part of the day-to-dayHands-on experience with distributed event streaming systems like Apache KafkaFamiliarity with OLAP databases such as Apache Pinot or ClickHouseProficient in modern data lake and warehouse tools such as S3, Databricks, or SnowflakeExperience with distributed data processing engines like Apache Flink or Apache SparkStrong foundation in the JVM ecosystem, container orchestration with Kubernetes, and cloud platforms, especially AWSEPIC JOB + EPIC BENEFITS = EPIC LIFEOur intent is to cover all things that are medically necessary and improve the quality of life. We pay 100% of the premiums for both you and your dependents. Our coverage includes Medical, Dental, a Vision HRA, Long Term Disability, Life Insurance & a 401k with competitive match. We also offer a robust mental well-being program through Modern Health, which provides free therapy and coaching for employees & dependents. Throughout the year we celebrate our employees with events and company-wide paid breaks. We offer unlimited PTO and sick time and recognize individuals for 7 years of employment with a paid sabbatical.ABOUT USEpic Games spans across 25 countries with 46 studios and 4,500+ employees globally. For over 25 years, we've been making award-winning games and engine technology that empowers others to make visually stunning games and 3D content that bring environments to life like never before. Epic's award-winning Unreal Engine technology not only provides game developers the ability to build high-fidelity, interactive experiences for PC, console, mobile, and VR, it is also a tool being embraced by content creators across a variety of industries such as media and entertainment, automotive, and architectural design. As we continue to build our Engine technology and develop remarkable games, we strive to build teams of world-class talent.Like what you hear? Come be a part of something Epic!Epic Games deeply values diverse teams and an inclusive work culture, and we are proud to be an Equal Opportunity employer. Learn more about our Equal Employment OpportunityPolicy here.Note to Recruitment Agencies: Epic does not accept any unsolicited resumes or approaches from any unauthorized third party. We will not pay any fees to any unauthorized third party. Further details on these matters can be found here. Create Your Profile — Game companies can contact you with their relevant job openings. Apply #engineering #lead #data #platform #epic

GAMEJOBS.CO

Engineering Lead, Data Platform at Epic Games

Engineering Lead, Data PlatformEpic GamesCary, North Carolina, United States11 hours agoApplyWHAT MAKES US EPIC?At the core of Epic’s success are talented, passionate people. Epic prides itself on creating a collaborative, welcoming, and creative environment. Whether it’s building award-winning games or crafting engine technology that enables others to make visually stunning interactive experiences, we’re always innovating.Being Epic means being a part of a team that continually strives to do right by our community and users. We’re constantly innovating to raise the bar of engine and game development.DATA ENGINEERINGWhat We DoOur mission is to provide a world-class platform that empowers the business to leverage data that will enhance, monitor, and support our products. We are responsible for data ingestion systems, processing pipelines, and various data stores all operating in the cloud. We operate at a petabyte scale, and support near real-time use cases as well as more traditional batch approaches.What You'll DoEpic Games is seeking a Senior Engineering Lead to guide the Data Services team, which builds and maintains the core services behind our data platform. This team handles telemetry collection, data schematization, stream routing, data lake integration, and real-time analytics, bridging platform, data, and backend engineering. In this role, you’ll lead team growth and mentorship, drive alignment on technical strategy, and collaborate cross-functionally to scale our data infrastructure.In this role, you willLead, mentor, and grow a team of senior and principal engineersFoster an inclusive, collaborative, and feedback-driven engineering cultureDrive continuous improvement in the team’s processes, delivery, and impactCollaborate with stakeholders in engineering, data science, and analytics to shape and communicate the team’s vision, strategy, and roadmapBridge strategic vision and tactical execution by breaking down long-term goals into achievable, well-scoped iterations that deliver continuous valueEnsure high standards in system architecture, code quality, and operational excellenceWhat we're looking for3+ years of engineering management experience leading high-performing teams in data platform or infrastructure environmentsProven track record navigating complex systems, ambiguous requirements, and high-pressure situations with confidence and clarityDeep experience in architecting, building, and operating scalable, distributed data platformsStrong technical leadership skills, including the ability to review architecture/design documents and provide actionable feedback on code and systemsAbility to engage deeply in technical discussions, review architecture and design documents, evaluate pull requests, and step in during high-priority incidents when needed — even if hands-on coding isn’t a part of the day-to-dayHands-on experience with distributed event streaming systems like Apache KafkaFamiliarity with OLAP databases such as Apache Pinot or ClickHouseProficient in modern data lake and warehouse tools such as S3, Databricks, or SnowflakeExperience with distributed data processing engines like Apache Flink or Apache SparkStrong foundation in the JVM ecosystem (Java, Kotlin, Scala), container orchestration with Kubernetes, and cloud platforms, especially AWSEPIC JOB + EPIC BENEFITS = EPIC LIFEOur intent is to cover all things that are medically necessary and improve the quality of life. We pay 100% of the premiums for both you and your dependents. Our coverage includes Medical, Dental, a Vision HRA, Long Term Disability, Life Insurance & a 401k with competitive match. We also offer a robust mental well-being program through Modern Health, which provides free therapy and coaching for employees & dependents. Throughout the year we celebrate our employees with events and company-wide paid breaks. We offer unlimited PTO and sick time and recognize individuals for 7 years of employment with a paid sabbatical.ABOUT USEpic Games spans across 25 countries with 46 studios and 4,500+ employees globally. For over 25 years, we've been making award-winning games and engine technology that empowers others to make visually stunning games and 3D content that bring environments to life like never before. Epic's award-winning Unreal Engine technology not only provides game developers the ability to build high-fidelity, interactive experiences for PC, console, mobile, and VR, it is also a tool being embraced by content creators across a variety of industries such as media and entertainment, automotive, and architectural design. As we continue to build our Engine technology and develop remarkable games, we strive to build teams of world-class talent.Like what you hear? Come be a part of something Epic!Epic Games deeply values diverse teams and an inclusive work culture, and we are proud to be an Equal Opportunity employer. Learn more about our Equal Employment Opportunity (EEO) Policy here.Note to Recruitment Agencies: Epic does not accept any unsolicited resumes or approaches from any unauthorized third party (including recruitment or placement agencies) (i.e., a third party with whom we do not have a negotiated and validly executed agreement). We will not pay any fees to any unauthorized third party. Further details on these matters can be found here. Create Your Profile — Game companies can contact you with their relevant job openings. Apply

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2025-05-31 11:16:51 ·

Drip Email Software: The Ultimate Pricing and Buying Guide

Reading Time: 11 minutes
Choosing the right drip email software can be a game-changer for your marketing strategy. With a variety of options available, understanding the pricing models and features each tool offers is key to making an informed decision.
In this guide, we’ll walk you through essential factors to consider when evaluating pricing, so you can select the best solution that fits your needs and budget for your drip email campaigns.
Ready to dive deeper? Let’s explore your options.

What Does Drip Email Marketing Software Cost?
Drip email marketing software typically follows a few different pricing models, depending on the provider. Here’s a quick breakdown of the most common ones:

Pricing Based on MAUs: You pay based on how many unique subscribers actually engage with your emailseach month, not your entire list size. It’s a much fairer, scalable way to pay, especially if your audience fluctuates. You only pay for real activity.
Flat-Rate Pricing: You pay a fixed monthly fee, regardless of usage, list size, or activity. But flat rates are often bloated to cover all usage types, so you’re likely overpaying if you’re not sending a massive volume of emails. It’s not cost-efficient for scaling brands.
List-Based Pricing: You pay based on the total number of contacts in your database, whether they engage or not. However, you end up paying for cold leads, dead emails, and inactive contacts. This makes your costs climb unnecessarily as your list grows.
Email Volume-Based Pricing: You’re charged based on how many emails you send each month. It can discourage you from running important nurturing campaigns or re-engagement flows because every email adds to your bill. It’s unpredictable and can spike costs during promotions. For email marketers, this model means added pressure to limit sends, often at the cost of customer experience and long-term engagement.

Some platforms even bundle unnecessary features into expensive tiers, which bloats the pricing, especially for features you may not actually need. This is where MoEngage stands out. The platform offers a more transparent and scalable approach so that pricing is aligned to your actual usage and outcomes rather than inflated send limits.

6 Biggest Factors that Affect Drip Email Pricing
Drip email pricing depends on several factors. Here are some of the biggest ones:
1. Number of active contacts
Some platforms, particularly the ones that use the MAU pricing model, charge based on the number of customers who interact with your emails. The higher the number of active customers, the higher the pricing.
2. List size
Managing an email list is easier said than done. Nevertheless, if you have a large database of contacts, you will be charged a higher monthly fee. In this case, pricing will remain the same even if you don’t have many active subscribers on your list.
3. Email volume
The number of emails you send plays a big role in pricing. This is because high-volume automated email marketing campaigns require significant infrastructure for email delivery, tracking, and reporting.
4. Advanced features
Platforms offering advanced features like RFM segmentation, automation, A/B testing, analytics, and AI-driven personalization typically charge more than others. Some pricing models allow you to opt for some or all of these features, depending on your business requirements.
5. Automation complexity
If your drip campaign involves complex workflows, like different stages, conditions, triggers, etc., it can impact the pricing. Automation requires more resources to set up and maintain, which increases the price.
6. Integration with other tools
Connecting your email platform with CRMs, Ecommerce tools, or analytics systems can drive up costs, especially if platforms charge extra for native integrations or require third-party connectors.
Before signing up, ask yourself: how many of these tools are truly essential? Can any be replaced with ones that integrate more easily? A cluttered tech stack not only costs more but also complicates your workflows.

Top Features to Look For in a Drip Email Marketing Software
Since you’re on the lookout for a drip email marketing software platform, focus on the features we’ve mentioned below.
Core features that drip email software absolutely needs
These features are the must-haves. They are the foundational tools required to automate, personalize, and optimize your email drip campaigns at scale.

Journey designer: Easily set up multi-step workflows triggered by parameters like customer actions, behaviors, or time delays with minimal coding.
Behavioral targeting: Send emails based on real-time customer behavior, such as cart additions, subscriptions, app activity, and more, for highly relevant messaging.
Personalization engine: Dynamically include customer attributes based on past interactions and preferences to personalize emails at scale.
High inbox deliverability: Tools and practices that ensure you have a healthy sender reputation and high inbox placement.
Campaign optimization with A/B testing: Test different email versions to learn what drives higher engagement and optimize your drip flows continuously.
Real-time analytics and insights: Access detailed reports and dashboards that track key drip email marketing metrics like opens, clicks, and conversions, and identify drop-offs in your drip journeys.

Other features that are very useful to have
These features aren’t absolutely necessary to run an effective campaign, but they can improve customer experience and operational efficiency.

AI-based predictive segmentation: Leverage AI to predict customer behavior like churn or likelihood of conversion, and automatically adjust campaigns accordingly.
Omnichannel messaging: Integrate messaging across various channels, like push notifications, SMS, WhatsApp, and in-app messaging for a cohesive customer journey.
Pre-built templates: Quickly access pre-built, customizable email templates to launch campaigns faster without heavy design work.

9 Steps to Choosing a Drip Email Marketing Software
With so many options available in the market, choosing the right drip email marketing software can seem like an uphill battle.
No worries! We’ll even walk you through exactly how to evaluate and compare drip email tools for yourself.

Once you’ve asked the right questions and gathered the facts during demos, it’s time to break down your decision-making process into clear, actionable steps.
Step 1: Define Your Non-Negotiable Features
The first step is to make a list of all the features that are necessary for you.
For example, if your goal is to send behavior-triggered drip campaigns, then features like behavioral targeting and multi-step customer journey automation are non-negotiable. On the other hand, if you only need basic scheduled newsletters, you might not need advanced workflow builders or AI recommendations.
Once you have a list of non-negotiable features, it becomes easier to narrow down your platform choices.
Step 2: Research and Compare All Viable Options
Don’t just go with the most well-known or even the most affordable-looking tool. Research platforms that fit your business size and industry.
Use peer reviews, comparison guides, and case studies to understand how the platform performs in real-world use cases.
Also, remember that low entry pricing can be misleading. What matters is the total cost of ownership. Look for things like hidden fees, integration costs, or feature-based tier upgrades. These will add to the pricing later.
Step 3: Book a Personalized DemoWhen choosing your drip email marketing tool, make sure you request a personalized demo based on your business context, use cases, and workflows.
A simple, generic walkthrough isn’t helpful because this is your first real glimpse into how the platform is going to support you long-term. If the demo isn’t tailored to your business, that’s a red flag—because if they can’t offer personalized attention when they’re trying to win your business, you’re unlikely to get it during onboarding or implementation.
Ask to see features live, rather than just screenshots or slide decks. If a feature is critical, ask to see it in action. At times, sales teams may promise features that are still ‘in development’.
Step 4: Attend the Demo with a List of Questions
Once you’ve booked a demo, prepare a checklist of questions, like the one we shared above. This will help you stay focused and gain the clarity you need.
Ask whether the features you care about are included in the base plan or locked behind premium tiers. Dig into pricing: will the costs scale predictably as you grow, or are there hidden fees that only show up later?
Some platforms may offer attractive deals upfront but raise prices later or charge extra for essentials like API access, automation workflows, or deliverability support. The demo is your chance to surface all of this before signing anything.
Step 5: Evaluate Ease of Use
The drip email software you choose must be easy to use. Since marketers with little to no technical knowledge are most likely to use the platform, look for solutions that allow you to build email campaigns, segment audiences, and personalize messages easily.
Say no to complicated or clunky interfaces that are not intuitive and require a steep learning curve.
Step 6: Check for Advanced Capabilities
Ask about AI features like predictive segmentation or email send-time optimization. Even if you don’t need them right now, having future-ready capabilities ensures you won’t outgrow the tool too soon.
Step 7: Test for Scalability and Deliverability
This is an important one. Look for solutions that can handle large volumes of emails with good email deliverability rates. Make sure the software you choose has a high inbox placement rate. Check if they have a solid infrastructure and dedicated IPs, should you need them. Also, consider scalability: will this platform still meet your needs when your contact list grows from 10,000 to 100,000? Make sure you’re not locked into expensive pricing jumps or performance bottlenecks.
Step 8: Review Integration Options
Make sure the tool you choose fits seamlessly into your existing workflow. It should integrate with your CRM, mobile app, Ecommerce system, and anything else in your ecosystem.
Even if it doesn’t directly integrate, ensure that it offers flexible APIs and webhooks, or supports third-party connectors like Zapier, so you can still automate data flow without manual workarounds.
Step 9: Compare Customer Support Quality
Good software with bad support can be a nightmare. Check for onboarding help, technical support, availability, account management, and resources. Ask about how quickly the support team responds and resolves software issues to ensure that you have minimal downtime.

5 Best Email Drip Campaign Software and Tools
It’s time to weigh your options in choosing an email drip campaign software for your business. Give this list a quick read!
1. MoEngage: Best for Omnichannel Engagement and Behavioral Targeting

MoEngage is a comprehensive customer engagement platform designed to deliver personalized and automated drip email campaigns at scale. Its powerful AI capabilities allow you to use behavioral targeting to reach customers at the right time with the right message at different stages of their customer journey.
Why MoEngage is Best for Omnichannel Engagement and Behavioral Targeting: MoEngage offers multiple features that allow you to provide cohesive customer experiences across various touchpoints. You can reach out to customers via not just email, but also SMS, push notifications, in-app messages, and even ad networks without missing a beat.
Features like advanced segmentation enable you to send hyper-personalized drip email campaigns tailored to individual customer actions, such as cart abandonment or inactivity. This ensures that messages are timely, relevant, and effective in driving engagement.
How Pricing Works: MoEngage offers flexible drip email pricing based on the scale of your business and the exact features you’d need. You can always reach out to their sales team to learn more about their custom pricing plans.
How to Set Up a Demo: To experience MoEngage firsthand, you need to book a demo first. Their team will then contact you to schedule a personalized walkthrough that showcases how the platform can meet your specific drip email marketing goals.
2. ActiveCampaign: Best for Email and Marketing Automation

ActiveCampaign is a marketing automation platform that allows you to create email campaigns on channels your customers connect with. The platform offers predictive content features that help you show customers the most meaningful content.
Why ActiveCampaign is Best for Email and Marketing Automation: ActiveCampaign excels in delivering sophisticated automation campaign workflows that can be tailored to various customer journeys.
How Pricing Works: ActiveCampaign offers a four-tiered drip email pricing plan. The basic plan starts at /month for 1K contacts, while the Enterprise plan starts at /month for 50K contacts. If you have contacts upwards of that number, you need to talk to their sales team.
3. Kit: Best for Creators Using Emails

Kit is tailored for creators, bloggers, and solo entrepreneurs who want to send customer relationship emails. It offers a user-friendly interface with powerful automation capabilities.
Why Kit is Best for Creators and Solo Entrepreneurs: Kit’s email-first operating system allows creators to build long-term connections with customers. Its intuitive email designer can be used to create effective on-brand emails that are optimized for deliverability. Plus, it allows you to send email broadcasts for announcing product launches, sharing content updates, or promoting limited-time offers to your entire list in bulk.
How Pricing Works: Kit offers a free Newsletter plan with basic features like visual automation, opt-in forms, and email broadcasts to up to 10K subscribers. But you can implement just 1 visual automation and an email sequence on the free plan. Advanced plans start at per month for up to 1K subscribers, and go up to /month for 400K contacts.
4. Drip: Best for Ecommerce Marketing Automation

Drip is a marketing automation platform designed specifically for Ecommerce brands. It offers tools to create personalized email campaigns that drive customer engagement and sales.
Why Drip is Best for Ecommerce Marketing Automation: Drip provides deep integrations with Ecommerce platforms, allowing businesses to leverage customer data for targeted campaigns. Features like behavior-based automation and pre-built workflows help in crafting effective drip campaigns.
How Pricing Works: Drip’s pricing starts at /month for up to 2,500 contacts. The cost scales up to /month based on the number of contacts, with custom pricing available for larger lists of over 180K subscribers.
5. Mailmodo: Best for Interactive Email Marketing

Mailmodo is an email marketing platform that enables the creation of interactive drip emails using AMP technology. This allows recipients to take actions like filling out forms or booking appointments directly within the email.
Why Mailmodo is Best for Interactive Email Marketing: Mailmodo stands out by offering interactive email capabilities that enhance customer engagement and conversion rates. Its drag-and-drop editor simplifies the creation of dynamic emails without any coding required.
How Pricing Works: Like the other drip email marketing software on this list, Mailmodo’s pricing is based on the number of contacts and emails sent per month. Its three-tiered pricing model starts at /month for 500 contacts, going up to /month for 250K subscribers. Any more contacts, and you have to reach out to their sales team.

How to Get the Most Value Out of Your Drip Email Software
Here are some of the ways in which you can leverage your drip email software to its maximum potential.

time through automation: Automate welcome emails, follow-ups, and re-engagement campaigns to free up your team for more strategic work.
Boost engagement with timely messaging: Set up triggers so that emails are automatically sent based on customers’ actions. This keeps your audience interested at every stage of their customer journey.
Drive higher conversions with targeted sequences: Send the right email at the right time to move leads through the sales funnel towards a conversion.
Personalize customer experiences at scale: Use segmentation and dynamic content to create a personalized customer experience without manually customizing every email.
Continuously optimize with data-driven insights: Track performance metrics like open rates and conversions to refine your drip email campaigns and maximize results over time.

Maximize Engagement With MoEngage’s Drip Email Marketing Software
To sum it up, drip email marketing can be quite powerful for engaging customers at different stages of their journey.
Having the right drip email software in your tech stack ensures smooth, efficient workflows. Your team can focus on higher-value tasks while automating routine processes. It also ensures that your drip emails always reach the right person with the right message at the perfect time.
MoEngage’s powerful email automation platform allows you to automate personalized campaigns that drive higher engagement and conversions. With advanced segmentation, behavior-triggered messaging, and seamless integration with your ecosystem, you can nurture relationships more effectively than ever.
Curious to see how MoEngage can elevate your campaigns? Schedule a discovery call today.
The post Drip Email Software: The Ultimate Pricing and Buying Guide appeared first on MoEngage.
#drip #email #software #ultimate #pricing

Drip Email Software: The Ultimate Pricing and Buying Guide
Reading Time: 11 minutes Choosing the right drip email software can be a game-changer for your marketing strategy. With a variety of options available, understanding the pricing models and features each tool offers is key to making an informed decision. In this guide, we’ll walk you through essential factors to consider when evaluating pricing, so you can select the best solution that fits your needs and budget for your drip email campaigns. Ready to dive deeper? Let’s explore your options. What Does Drip Email Marketing Software Cost? Drip email marketing software typically follows a few different pricing models, depending on the provider. Here’s a quick breakdown of the most common ones: Pricing Based on MAUs: You pay based on how many unique subscribers actually engage with your emailseach month, not your entire list size. It’s a much fairer, scalable way to pay, especially if your audience fluctuates. You only pay for real activity. Flat-Rate Pricing: You pay a fixed monthly fee, regardless of usage, list size, or activity. But flat rates are often bloated to cover all usage types, so you’re likely overpaying if you’re not sending a massive volume of emails. It’s not cost-efficient for scaling brands. List-Based Pricing: You pay based on the total number of contacts in your database, whether they engage or not. However, you end up paying for cold leads, dead emails, and inactive contacts. This makes your costs climb unnecessarily as your list grows. Email Volume-Based Pricing: You’re charged based on how many emails you send each month. It can discourage you from running important nurturing campaigns or re-engagement flows because every email adds to your bill. It’s unpredictable and can spike costs during promotions. For email marketers, this model means added pressure to limit sends, often at the cost of customer experience and long-term engagement. Some platforms even bundle unnecessary features into expensive tiers, which bloats the pricing, especially for features you may not actually need. This is where MoEngage stands out. The platform offers a more transparent and scalable approach so that pricing is aligned to your actual usage and outcomes rather than inflated send limits. 6 Biggest Factors that Affect Drip Email Pricing Drip email pricing depends on several factors. Here are some of the biggest ones: 1. Number of active contacts Some platforms, particularly the ones that use the MAU pricing model, charge based on the number of customers who interact with your emails. The higher the number of active customers, the higher the pricing. 2. List size Managing an email list is easier said than done. Nevertheless, if you have a large database of contacts, you will be charged a higher monthly fee. In this case, pricing will remain the same even if you don’t have many active subscribers on your list. 3. Email volume The number of emails you send plays a big role in pricing. This is because high-volume automated email marketing campaigns require significant infrastructure for email delivery, tracking, and reporting. 4. Advanced features Platforms offering advanced features like RFM segmentation, automation, A/B testing, analytics, and AI-driven personalization typically charge more than others. Some pricing models allow you to opt for some or all of these features, depending on your business requirements. 5. Automation complexity If your drip campaign involves complex workflows, like different stages, conditions, triggers, etc., it can impact the pricing. Automation requires more resources to set up and maintain, which increases the price. 6. Integration with other tools Connecting your email platform with CRMs, Ecommerce tools, or analytics systems can drive up costs, especially if platforms charge extra for native integrations or require third-party connectors. Before signing up, ask yourself: how many of these tools are truly essential? Can any be replaced with ones that integrate more easily? A cluttered tech stack not only costs more but also complicates your workflows. Top Features to Look For in a Drip Email Marketing Software Since you’re on the lookout for a drip email marketing software platform, focus on the features we’ve mentioned below. Core features that drip email software absolutely needs These features are the must-haves. They are the foundational tools required to automate, personalize, and optimize your email drip campaigns at scale. Journey designer: Easily set up multi-step workflows triggered by parameters like customer actions, behaviors, or time delays with minimal coding. Behavioral targeting: Send emails based on real-time customer behavior, such as cart additions, subscriptions, app activity, and more, for highly relevant messaging. Personalization engine: Dynamically include customer attributes based on past interactions and preferences to personalize emails at scale. High inbox deliverability: Tools and practices that ensure you have a healthy sender reputation and high inbox placement. Campaign optimization with A/B testing: Test different email versions to learn what drives higher engagement and optimize your drip flows continuously. Real-time analytics and insights: Access detailed reports and dashboards that track key drip email marketing metrics like opens, clicks, and conversions, and identify drop-offs in your drip journeys. Other features that are very useful to have These features aren’t absolutely necessary to run an effective campaign, but they can improve customer experience and operational efficiency. AI-based predictive segmentation: Leverage AI to predict customer behavior like churn or likelihood of conversion, and automatically adjust campaigns accordingly. Omnichannel messaging: Integrate messaging across various channels, like push notifications, SMS, WhatsApp, and in-app messaging for a cohesive customer journey. Pre-built templates: Quickly access pre-built, customizable email templates to launch campaigns faster without heavy design work. 9 Steps to Choosing a Drip Email Marketing Software With so many options available in the market, choosing the right drip email marketing software can seem like an uphill battle. No worries! We’ll even walk you through exactly how to evaluate and compare drip email tools for yourself. Once you’ve asked the right questions and gathered the facts during demos, it’s time to break down your decision-making process into clear, actionable steps. Step 1: Define Your Non-Negotiable Features The first step is to make a list of all the features that are necessary for you. For example, if your goal is to send behavior-triggered drip campaigns, then features like behavioral targeting and multi-step customer journey automation are non-negotiable. On the other hand, if you only need basic scheduled newsletters, you might not need advanced workflow builders or AI recommendations. Once you have a list of non-negotiable features, it becomes easier to narrow down your platform choices. Step 2: Research and Compare All Viable Options Don’t just go with the most well-known or even the most affordable-looking tool. Research platforms that fit your business size and industry. Use peer reviews, comparison guides, and case studies to understand how the platform performs in real-world use cases. Also, remember that low entry pricing can be misleading. What matters is the total cost of ownership. Look for things like hidden fees, integration costs, or feature-based tier upgrades. These will add to the pricing later. Step 3: Book a Personalized DemoWhen choosing your drip email marketing tool, make sure you request a personalized demo based on your business context, use cases, and workflows. A simple, generic walkthrough isn’t helpful because this is your first real glimpse into how the platform is going to support you long-term. If the demo isn’t tailored to your business, that’s a red flag—because if they can’t offer personalized attention when they’re trying to win your business, you’re unlikely to get it during onboarding or implementation. Ask to see features live, rather than just screenshots or slide decks. If a feature is critical, ask to see it in action. At times, sales teams may promise features that are still ‘in development’. Step 4: Attend the Demo with a List of Questions Once you’ve booked a demo, prepare a checklist of questions, like the one we shared above. This will help you stay focused and gain the clarity you need. Ask whether the features you care about are included in the base plan or locked behind premium tiers. Dig into pricing: will the costs scale predictably as you grow, or are there hidden fees that only show up later? Some platforms may offer attractive deals upfront but raise prices later or charge extra for essentials like API access, automation workflows, or deliverability support. The demo is your chance to surface all of this before signing anything. Step 5: Evaluate Ease of Use The drip email software you choose must be easy to use. Since marketers with little to no technical knowledge are most likely to use the platform, look for solutions that allow you to build email campaigns, segment audiences, and personalize messages easily. Say no to complicated or clunky interfaces that are not intuitive and require a steep learning curve. Step 6: Check for Advanced Capabilities Ask about AI features like predictive segmentation or email send-time optimization. Even if you don’t need them right now, having future-ready capabilities ensures you won’t outgrow the tool too soon. Step 7: Test for Scalability and Deliverability This is an important one. Look for solutions that can handle large volumes of emails with good email deliverability rates. Make sure the software you choose has a high inbox placement rate. Check if they have a solid infrastructure and dedicated IPs, should you need them. Also, consider scalability: will this platform still meet your needs when your contact list grows from 10,000 to 100,000? Make sure you’re not locked into expensive pricing jumps or performance bottlenecks. Step 8: Review Integration Options Make sure the tool you choose fits seamlessly into your existing workflow. It should integrate with your CRM, mobile app, Ecommerce system, and anything else in your ecosystem. Even if it doesn’t directly integrate, ensure that it offers flexible APIs and webhooks, or supports third-party connectors like Zapier, so you can still automate data flow without manual workarounds. Step 9: Compare Customer Support Quality Good software with bad support can be a nightmare. Check for onboarding help, technical support, availability, account management, and resources. Ask about how quickly the support team responds and resolves software issues to ensure that you have minimal downtime. 5 Best Email Drip Campaign Software and Tools It’s time to weigh your options in choosing an email drip campaign software for your business. Give this list a quick read! 1. MoEngage: Best for Omnichannel Engagement and Behavioral Targeting MoEngage is a comprehensive customer engagement platform designed to deliver personalized and automated drip email campaigns at scale. Its powerful AI capabilities allow you to use behavioral targeting to reach customers at the right time with the right message at different stages of their customer journey. Why MoEngage is Best for Omnichannel Engagement and Behavioral Targeting: MoEngage offers multiple features that allow you to provide cohesive customer experiences across various touchpoints. You can reach out to customers via not just email, but also SMS, push notifications, in-app messages, and even ad networks without missing a beat. Features like advanced segmentation enable you to send hyper-personalized drip email campaigns tailored to individual customer actions, such as cart abandonment or inactivity. This ensures that messages are timely, relevant, and effective in driving engagement. How Pricing Works: MoEngage offers flexible drip email pricing based on the scale of your business and the exact features you’d need. You can always reach out to their sales team to learn more about their custom pricing plans. How to Set Up a Demo: To experience MoEngage firsthand, you need to book a demo first. Their team will then contact you to schedule a personalized walkthrough that showcases how the platform can meet your specific drip email marketing goals. 2. ActiveCampaign: Best for Email and Marketing Automation ActiveCampaign is a marketing automation platform that allows you to create email campaigns on channels your customers connect with. The platform offers predictive content features that help you show customers the most meaningful content. Why ActiveCampaign is Best for Email and Marketing Automation: ActiveCampaign excels in delivering sophisticated automation campaign workflows that can be tailored to various customer journeys. How Pricing Works: ActiveCampaign offers a four-tiered drip email pricing plan. The basic plan starts at /month for 1K contacts, while the Enterprise plan starts at /month for 50K contacts. If you have contacts upwards of that number, you need to talk to their sales team. 3. Kit: Best for Creators Using Emails Kit is tailored for creators, bloggers, and solo entrepreneurs who want to send customer relationship emails. It offers a user-friendly interface with powerful automation capabilities. Why Kit is Best for Creators and Solo Entrepreneurs: Kit’s email-first operating system allows creators to build long-term connections with customers. Its intuitive email designer can be used to create effective on-brand emails that are optimized for deliverability. Plus, it allows you to send email broadcasts for announcing product launches, sharing content updates, or promoting limited-time offers to your entire list in bulk. How Pricing Works: Kit offers a free Newsletter plan with basic features like visual automation, opt-in forms, and email broadcasts to up to 10K subscribers. But you can implement just 1 visual automation and an email sequence on the free plan. Advanced plans start at per month for up to 1K subscribers, and go up to /month for 400K contacts. 4. Drip: Best for Ecommerce Marketing Automation Drip is a marketing automation platform designed specifically for Ecommerce brands. It offers tools to create personalized email campaigns that drive customer engagement and sales. Why Drip is Best for Ecommerce Marketing Automation: Drip provides deep integrations with Ecommerce platforms, allowing businesses to leverage customer data for targeted campaigns. Features like behavior-based automation and pre-built workflows help in crafting effective drip campaigns. How Pricing Works: Drip’s pricing starts at /month for up to 2,500 contacts. The cost scales up to /month based on the number of contacts, with custom pricing available for larger lists of over 180K subscribers. 5. Mailmodo: Best for Interactive Email Marketing Mailmodo is an email marketing platform that enables the creation of interactive drip emails using AMP technology. This allows recipients to take actions like filling out forms or booking appointments directly within the email. Why Mailmodo is Best for Interactive Email Marketing: Mailmodo stands out by offering interactive email capabilities that enhance customer engagement and conversion rates. Its drag-and-drop editor simplifies the creation of dynamic emails without any coding required. How Pricing Works: Like the other drip email marketing software on this list, Mailmodo’s pricing is based on the number of contacts and emails sent per month. Its three-tiered pricing model starts at /month for 500 contacts, going up to /month for 250K subscribers. Any more contacts, and you have to reach out to their sales team. How to Get the Most Value Out of Your Drip Email Software Here are some of the ways in which you can leverage your drip email software to its maximum potential. time through automation: Automate welcome emails, follow-ups, and re-engagement campaigns to free up your team for more strategic work. Boost engagement with timely messaging: Set up triggers so that emails are automatically sent based on customers’ actions. This keeps your audience interested at every stage of their customer journey. Drive higher conversions with targeted sequences: Send the right email at the right time to move leads through the sales funnel towards a conversion. Personalize customer experiences at scale: Use segmentation and dynamic content to create a personalized customer experience without manually customizing every email. Continuously optimize with data-driven insights: Track performance metrics like open rates and conversions to refine your drip email campaigns and maximize results over time. Maximize Engagement With MoEngage’s Drip Email Marketing Software To sum it up, drip email marketing can be quite powerful for engaging customers at different stages of their journey. Having the right drip email software in your tech stack ensures smooth, efficient workflows. Your team can focus on higher-value tasks while automating routine processes. It also ensures that your drip emails always reach the right person with the right message at the perfect time. MoEngage’s powerful email automation platform allows you to automate personalized campaigns that drive higher engagement and conversions. With advanced segmentation, behavior-triggered messaging, and seamless integration with your ecosystem, you can nurture relationships more effectively than ever. Curious to see how MoEngage can elevate your campaigns? Schedule a discovery call today. The post Drip Email Software: The Ultimate Pricing and Buying Guide appeared first on MoEngage. #drip #email #software #ultimate #pricing

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Drip Email Software: The Ultimate Pricing and Buying Guide

Reading Time: 11 minutes Choosing the right drip email software can be a game-changer for your marketing strategy. With a variety of options available, understanding the pricing models and features each tool offers is key to making an informed decision. In this guide, we’ll walk you through essential factors to consider when evaluating pricing, so you can select the best solution that fits your needs and budget for your drip email campaigns. Ready to dive deeper? Let’s explore your options. What Does Drip Email Marketing Software Cost? Drip email marketing software typically follows a few different pricing models, depending on the provider. Here’s a quick breakdown of the most common ones: Pricing Based on MAUs (Monthly Active Users): You pay based on how many unique subscribers actually engage with your emails (or your system) each month, not your entire list size. It’s a much fairer, scalable way to pay, especially if your audience fluctuates. You only pay for real activity. Flat-Rate Pricing: You pay a fixed monthly fee, regardless of usage, list size, or activity. But flat rates are often bloated to cover all usage types, so you’re likely overpaying if you’re not sending a massive volume of emails. It’s not cost-efficient for scaling brands. List-Based Pricing: You pay based on the total number of contacts in your database, whether they engage or not. However, you end up paying for cold leads, dead emails, and inactive contacts. This makes your costs climb unnecessarily as your list grows. Email Volume-Based Pricing: You’re charged based on how many emails you send each month. It can discourage you from running important nurturing campaigns or re-engagement flows because every email adds to your bill. It’s unpredictable and can spike costs during promotions. For email marketers, this model means added pressure to limit sends, often at the cost of customer experience and long-term engagement. Some platforms even bundle unnecessary features into expensive tiers, which bloats the pricing, especially for features you may not actually need. This is where MoEngage stands out. The platform offers a more transparent and scalable approach so that pricing is aligned to your actual usage and outcomes rather than inflated send limits. 6 Biggest Factors that Affect Drip Email Pricing Drip email pricing depends on several factors. Here are some of the biggest ones: 1. Number of active contacts Some platforms, particularly the ones that use the MAU pricing model, charge based on the number of customers who interact with your emails. The higher the number of active customers, the higher the pricing. 2. List size Managing an email list is easier said than done. Nevertheless, if you have a large database of contacts, you will be charged a higher monthly fee. In this case, pricing will remain the same even if you don’t have many active subscribers on your list. 3. Email volume The number of emails you send plays a big role in pricing. This is because high-volume automated email marketing campaigns require significant infrastructure for email delivery, tracking, and reporting. 4. Advanced features Platforms offering advanced features like RFM segmentation, automation, A/B testing, analytics, and AI-driven personalization typically charge more than others. Some pricing models allow you to opt for some or all of these features, depending on your business requirements. 5. Automation complexity If your drip campaign involves complex workflows, like different stages, conditions, triggers, etc., it can impact the pricing. Automation requires more resources to set up and maintain, which increases the price. 6. Integration with other tools Connecting your email platform with CRMs, Ecommerce tools, or analytics systems can drive up costs, especially if platforms charge extra for native integrations or require third-party connectors. Before signing up, ask yourself: how many of these tools are truly essential? Can any be replaced with ones that integrate more easily? A cluttered tech stack not only costs more but also complicates your workflows. Top Features to Look For in a Drip Email Marketing Software Since you’re on the lookout for a drip email marketing software platform, focus on the features we’ve mentioned below. Core features that drip email software absolutely needs These features are the must-haves. They are the foundational tools required to automate, personalize, and optimize your email drip campaigns at scale. Journey designer: Easily set up multi-step workflows triggered by parameters like customer actions, behaviors, or time delays with minimal coding. Behavioral targeting: Send emails based on real-time customer behavior, such as cart additions, subscriptions, app activity, and more, for highly relevant messaging. Personalization engine: Dynamically include customer attributes based on past interactions and preferences to personalize emails at scale. High inbox deliverability: Tools and practices that ensure you have a healthy sender reputation and high inbox placement. Campaign optimization with A/B testing: Test different email versions to learn what drives higher engagement and optimize your drip flows continuously. Real-time analytics and insights: Access detailed reports and dashboards that track key drip email marketing metrics like opens, clicks, and conversions, and identify drop-offs in your drip journeys. Other features that are very useful to have These features aren’t absolutely necessary to run an effective campaign, but they can improve customer experience and operational efficiency. AI-based predictive segmentation: Leverage AI to predict customer behavior like churn or likelihood of conversion, and automatically adjust campaigns accordingly. Omnichannel messaging: Integrate messaging across various channels, like push notifications, SMS, WhatsApp, and in-app messaging for a cohesive customer journey. Pre-built templates: Quickly access pre-built, customizable email templates to launch campaigns faster without heavy design work. 9 Steps to Choosing a Drip Email Marketing Software With so many options available in the market, choosing the right drip email marketing software can seem like an uphill battle. No worries! We’ll even walk you through exactly how to evaluate and compare drip email tools for yourself. Once you’ve asked the right questions and gathered the facts during demos, it’s time to break down your decision-making process into clear, actionable steps. Step 1: Define Your Non-Negotiable Features The first step is to make a list of all the features that are necessary for you. For example, if your goal is to send behavior-triggered drip campaigns (like abandoned cart emails or re-engagement nudges), then features like behavioral targeting and multi-step customer journey automation are non-negotiable. On the other hand, if you only need basic scheduled newsletters, you might not need advanced workflow builders or AI recommendations. Once you have a list of non-negotiable features, it becomes easier to narrow down your platform choices. Step 2: Research and Compare All Viable Options Don’t just go with the most well-known or even the most affordable-looking tool. Research platforms that fit your business size and industry. Use peer reviews, comparison guides, and case studies to understand how the platform performs in real-world use cases. Also, remember that low entry pricing can be misleading. What matters is the total cost of ownership. Look for things like hidden fees, integration costs, or feature-based tier upgrades. These will add to the pricing later. Step 3: Book a Personalized Demo (and Demand Proof) When choosing your drip email marketing tool, make sure you request a personalized demo based on your business context, use cases, and workflows. A simple, generic walkthrough isn’t helpful because this is your first real glimpse into how the platform is going to support you long-term. If the demo isn’t tailored to your business, that’s a red flag—because if they can’t offer personalized attention when they’re trying to win your business, you’re unlikely to get it during onboarding or implementation. Ask to see features live, rather than just screenshots or slide decks. If a feature is critical, ask to see it in action. At times, sales teams may promise features that are still ‘in development’. Step 4: Attend the Demo with a List of Questions Once you’ve booked a demo, prepare a checklist of questions, like the one we shared above. This will help you stay focused and gain the clarity you need. Ask whether the features you care about are included in the base plan or locked behind premium tiers. Dig into pricing: will the costs scale predictably as you grow, or are there hidden fees that only show up later? Some platforms may offer attractive deals upfront but raise prices later or charge extra for essentials like API access, automation workflows, or deliverability support. The demo is your chance to surface all of this before signing anything. Step 5: Evaluate Ease of Use The drip email software you choose must be easy to use. Since marketers with little to no technical knowledge are most likely to use the platform, look for solutions that allow you to build email campaigns, segment audiences, and personalize messages easily. Say no to complicated or clunky interfaces that are not intuitive and require a steep learning curve. Step 6: Check for Advanced Capabilities Ask about AI features like predictive segmentation or email send-time optimization. Even if you don’t need them right now, having future-ready capabilities ensures you won’t outgrow the tool too soon. Step 7: Test for Scalability and Deliverability This is an important one. Look for solutions that can handle large volumes of emails with good email deliverability rates. Make sure the software you choose has a high inbox placement rate. Check if they have a solid infrastructure and dedicated IPs, should you need them. Also, consider scalability: will this platform still meet your needs when your contact list grows from 10,000 to 100,000? Make sure you’re not locked into expensive pricing jumps or performance bottlenecks. Step 8: Review Integration Options Make sure the tool you choose fits seamlessly into your existing workflow. It should integrate with your CRM, mobile app, Ecommerce system, and anything else in your ecosystem. Even if it doesn’t directly integrate, ensure that it offers flexible APIs and webhooks, or supports third-party connectors like Zapier, so you can still automate data flow without manual workarounds. Step 9: Compare Customer Support Quality Good software with bad support can be a nightmare. Check for onboarding help, technical support, availability, account management, and resources. Ask about how quickly the support team responds and resolves software issues to ensure that you have minimal downtime. 5 Best Email Drip Campaign Software and Tools It’s time to weigh your options in choosing an email drip campaign software for your business. Give this list a quick read! 1. MoEngage: Best for Omnichannel Engagement and Behavioral Targeting MoEngage is a comprehensive customer engagement platform designed to deliver personalized and automated drip email campaigns at scale. Its powerful AI capabilities allow you to use behavioral targeting to reach customers at the right time with the right message at different stages of their customer journey. Why MoEngage is Best for Omnichannel Engagement and Behavioral Targeting: MoEngage offers multiple features that allow you to provide cohesive customer experiences across various touchpoints. You can reach out to customers via not just email, but also SMS, push notifications, in-app messages, and even ad networks without missing a beat. Features like advanced segmentation enable you to send hyper-personalized drip email campaigns tailored to individual customer actions, such as cart abandonment or inactivity. This ensures that messages are timely, relevant, and effective in driving engagement. How Pricing Works: MoEngage offers flexible drip email pricing based on the scale of your business and the exact features you’d need. You can always reach out to their sales team to learn more about their custom pricing plans. How to Set Up a Demo: To experience MoEngage firsthand, you need to book a demo first. Their team will then contact you to schedule a personalized walkthrough that showcases how the platform can meet your specific drip email marketing goals. 2. ActiveCampaign: Best for Email and Marketing Automation ActiveCampaign is a marketing automation platform that allows you to create email campaigns on channels your customers connect with. The platform offers predictive content features that help you show customers the most meaningful content. Why ActiveCampaign is Best for Email and Marketing Automation: ActiveCampaign excels in delivering sophisticated automation campaign workflows that can be tailored to various customer journeys. How Pricing Works: ActiveCampaign offers a four-tiered drip email pricing plan. The basic plan starts at $15/month for 1K contacts, while the Enterprise plan starts at $1,169/month for 50K contacts. If you have contacts upwards of that number, you need to talk to their sales team. 3. Kit: Best for Creators Using Emails Kit is tailored for creators, bloggers, and solo entrepreneurs who want to send customer relationship emails. It offers a user-friendly interface with powerful automation capabilities. Why Kit is Best for Creators and Solo Entrepreneurs: Kit’s email-first operating system allows creators to build long-term connections with customers. Its intuitive email designer can be used to create effective on-brand emails that are optimized for deliverability. Plus, it allows you to send email broadcasts for announcing product launches, sharing content updates, or promoting limited-time offers to your entire list in bulk. How Pricing Works: Kit offers a free Newsletter plan with basic features like visual automation, opt-in forms, and email broadcasts to up to 10K subscribers. But you can implement just 1 visual automation and an email sequence on the free plan. Advanced plans start at $25 per month for up to 1K subscribers, and go up to $2,166/month for 400K contacts. 4. Drip: Best for Ecommerce Marketing Automation Drip is a marketing automation platform designed specifically for Ecommerce brands. It offers tools to create personalized email campaigns that drive customer engagement and sales. Why Drip is Best for Ecommerce Marketing Automation: Drip provides deep integrations with Ecommerce platforms, allowing businesses to leverage customer data for targeted campaigns. Features like behavior-based automation and pre-built workflows help in crafting effective drip campaigns. How Pricing Works: Drip’s pricing starts at $39/month for up to 2,500 contacts. The cost scales up to $1,999/month based on the number of contacts, with custom pricing available for larger lists of over 180K subscribers. 5. Mailmodo: Best for Interactive Email Marketing Mailmodo is an email marketing platform that enables the creation of interactive drip emails using AMP technology. This allows recipients to take actions like filling out forms or booking appointments directly within the email. Why Mailmodo is Best for Interactive Email Marketing: Mailmodo stands out by offering interactive email capabilities that enhance customer engagement and conversion rates. Its drag-and-drop editor simplifies the creation of dynamic emails without any coding required. How Pricing Works: Like the other drip email marketing software on this list, Mailmodo’s pricing is based on the number of contacts and emails sent per month. Its three-tiered pricing model starts at $39/month for 500 contacts, going up to $939/month for 250K subscribers. Any more contacts, and you have to reach out to their sales team. How to Get the Most Value Out of Your Drip Email Software Here are some of the ways in which you can leverage your drip email software to its maximum potential. Save time through automation: Automate welcome emails, follow-ups, and re-engagement campaigns to free up your team for more strategic work. Boost engagement with timely messaging: Set up triggers so that emails are automatically sent based on customers’ actions. This keeps your audience interested at every stage of their customer journey. Drive higher conversions with targeted sequences: Send the right email at the right time to move leads through the sales funnel towards a conversion. Personalize customer experiences at scale: Use segmentation and dynamic content to create a personalized customer experience without manually customizing every email. Continuously optimize with data-driven insights: Track performance metrics like open rates and conversions to refine your drip email campaigns and maximize results over time. Maximize Engagement With MoEngage’s Drip Email Marketing Software To sum it up, drip email marketing can be quite powerful for engaging customers at different stages of their journey. Having the right drip email software in your tech stack ensures smooth, efficient workflows. Your team can focus on higher-value tasks while automating routine processes. It also ensures that your drip emails always reach the right person with the right message at the perfect time. MoEngage’s powerful email automation platform allows you to automate personalized campaigns that drive higher engagement and conversions. With advanced segmentation, behavior-triggered messaging, and seamless integration with your ecosystem, you can nurture relationships more effectively than ever. Curious to see how MoEngage can elevate your campaigns? Schedule a discovery call today. The post Drip Email Software: The Ultimate Pricing and Buying Guide appeared first on MoEngage.

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