Debug every step of your RAG pipeline: Retrieve → Rerank → Generate
The workbench for enterprise search and knowledge base Q&A teams to visualize, diagnose, and optimize retrieval-augmented generation systems.
See exactly which chunks your vector DB returns and why
Analyze how reranking changes document order and relevance
Track how context influences LLM output quality
Interactive dashboards showing chunk relevance scores, embedding distances, and retrieval patterns across queries.
Tag and categorize retrieval failures. Build datasets to systematically improve your RAG system.
Run controlled experiments comparing chunking strategies, embedding models, and reranking approaches.
Weekly insights on retrieval quality, failure trends, and optimization opportunities.
Debug internal knowledge search systems. Understand why employees can't find the right documents.
Optimize customer-facing chatbots and support systems for accurate, grounded answers.
Iterate faster on RAG architectures with clear visibility into each pipeline stage.
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