RAG Implementation
Ground AI answers in your own knowledge with retrieval-augmented generation. Ingestion, search, reranking and citations, evaluated so the answers are accurate and traceable.
Answers grounded in your data
Retrieval-augmented generation connects a language model to your own knowledge so it answers from your documents rather than its training data. Done right, it is the difference between a plausible answer and a correct, sourced one.
We build the full pipeline: ingestion, chunking, search, reranking and citation, and we evaluate retrieval quality directly, because a RAG system is only as good as what it retrieves.
- Grounded, cited answers from your own knowledge.
- Retrieval quality evaluated directly, not just the final answer.
- Freshness and access control handled from the start.
What a RAG build includes
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Ingestion and indexing
Pipelines that ingest your documents and data, chunk them well, and keep the index fresh as content changes.
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Search and reranking
Hybrid search and reranking so the model is given the right context, with access control respected throughout.
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Citations and evaluation
Answers traced to sources, plus evaluation of retrieval and answer quality against real questions.
Frequently asked questions
Why RAG instead of fine-tuning?
RAG grounds answers in current, controllable knowledge and updates as your content changes. It is usually faster, cheaper and more transparent than fine-tuning for knowledge-heavy tasks.
How do you keep answers accurate?
We evaluate retrieval quality directly and require citations, so answers are traceable to a source and low-confidence cases can defer.
Can it respect who is allowed to see what?
Yes. Access control is enforced in retrieval, so users only ever get answers from documents they are permitted to see.
Ground your AI in what your business knows
Tell us the knowledge you want AI to draw on. We will scope a RAG system that answers accurately and cites its sources.