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RAG development services
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
When it fits
- Answers must come from documents you control, and users need to see the source.
- Your content changes often, so retraining a model every time would not work.
- You have enough documents that people cannot find things with search alone.
When it does not
- The whole corpus fits in a single prompt. Skip the retrieval layer.
- The documents are mostly scans or tables that nobody has cleaned. Fix the source first.
- You expect the system to reason across thousands of documents at once. Retrieval returns passages, not the whole picture.
What we need from you
- A sample of the real documents, including the messy ones.
- Twenty or more questions your users ask, with the answers you consider correct.
- Rules for who may see which documents.
Ready to move from AI experiments to AI in production?
A thirty-minute call. We assess the use case, name the risks, and tell you honestly whether we are the right fit.