RAG Development (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation) development builds a system that looks up relevant information from your documents before an AI model answers, so responses are grounded in your real content rather than the model's general training.
Benefits
- Answers grounded in your actual documents, reducing incorrect responses
- Easy to update — add a document, the assistant knows it
- Works with large document sets that wouldn't fit in a single prompt
Problems It Solves
- AI assistants giving plausible but incorrect answers
- Document sets too large to hand-feed into an AI tool
- Needing an assistant that stays current as documents change
Who It's For
- Businesses with large knowledge bases
- Legal and compliance-heavy industries
- Support teams
Common Use Cases
- An internal assistant answering from policy and procedure documents
- A support bot answering from product documentation
- A research tool searching across contracts or reports
How We Deliver It
- 1
Document preparation
We prepare and chunk your documents for retrieval.
- 2
Retrieval build
We build the search and retrieval layer using vector embeddings.
- 3
Answer tuning
We tune how the AI uses retrieved content to answer accurately.
Technologies
- OpenAI API
- Vector embeddings
- Supabase pgvector
FAQs
Tell us what you're trying to solve.
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