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RAG and knowledge retrieval
Connect private data to a model without guessing whether it worked.
What this actually involves
Retrieval is where most generative AI projects quietly fail. Chunking strategy, embedding choice, and re-ranking each move accuracy more than the model does, and none of it is visible without evaluation. We build the pipeline and the harness that measures it together.
- Document processing and chunking tuned to your corpus
- Vector store integration with hybrid keyword and semantic search
- Semantic re-ranking and citation grounding
- An evaluation set your team can run on every change
Ready to move from AI experiments to AI in production?
Thirty minutes with a senior engineer. We assess the use case, name the risks, and tell you honestly whether we are the right fit.