Making RAG systems reliable, one pipeline at a time.
RAG (Retrieval-Augmented Generation) is transforming how enterprises build AI applications. But debugging RAG systems is hard. When your chatbot gives wrong answers, is it a retrieval problem? A reranking issue? A prompt problem? Without visibility into each stage, teams waste weeks guessing.
We built RAGDebugger to give teams full visibility into their RAG pipelines. Our platform helps you visualize retrieval results, label failures, run experiments, and systematically improve your system's accuracy.
RAGDebugger was founded in 2025 by a team of ML engineers who spent years building enterprise search and Q&A systems. We experienced firsthand the pain of debugging RAG pipelines with nothing but log files and intuition.
After building internal tools at multiple companies, we realized every team was solving the same problems. So we built RAGDebugger to be the debugging workbench we always wanted.
We believe in making AI systems understandable. Every decision in your RAG pipeline should be visible and explainable.
Intuition isn't enough. We help teams make decisions based on metrics, experiments, and systematic analysis.
Built by engineers, for engineers. We prioritize great APIs, clear documentation, and seamless integrations.
CEO & Co-founder
CTO & Co-founder
Head of ML
Head of Product