RAG pipeline development
Retrieval-augmented generation lets your team query your documents, contracts, and internal knowledge with natural language, and get accurate, cited answers. No hallucinations, no black boxes.
Pricing on request
RAG or fine-tuning: which do you need?
RAG is best when your knowledge changes frequently or you need source citations. Fine-tuning is better for style, format, or behavioral changes. We help you choose.
What we build
- Document ingestion & chunking pipeline
- Vector database (Pinecone, Weaviate, pgvector)
- Embedding model selection & tuning
- Retrieval strategy optimization
- Query interface (API or UI)
- Source citation & attribution
- Evaluation & accuracy testing
Common use cases
- Contract and legal document search
- Clinical trial data querying
- Internal policy & procedure search
- Customer support knowledge base
- Research literature review
- Regulatory document compliance
Ready to query your documents?
Book a free call to discuss your document types and query patterns.
Related
- RAG vs fine-tuning
Which approach fits your use case, and why the answer is usually RAG.
- RAG for biotech
Clinical data and regulatory document querying under HIPAA.
- RAG for legal teams
Contract review and due diligence pipelines with source citations.
- Deployed RAG systems
What we have shipped, with the metrics that came out of it.
Start with one workflow
Book a free 30-minute AI audit. We look at one workflow with you and tell you whether it can reach production in 6 to 8 weeks. No commitment, just 30 minutes with an engineer.
Book a free AI audit