Ingest your private corpus
PDFs, wikis, tickets, and databases chunked, embedded, and kept in sync — so answers never go stale.
RAG System Development
RAG that answers from your documents, not the internet
Retrieval-Augmented Generation for teams that cannot afford hallucinated answers. We build the ingestion pipeline, vector index, and chat or search UI so your knowledge base becomes a product feature.
Typical production RAG assistant: 3–6 weeks.
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What you get
A working knowledge assistant — not a notebook demo.
Retrieve before you generate
Semantic search over Pinecone, Qdrant, or pgvector pulls the right passages before the model speaks.
Stay inside your perimeter
Access control, audit trails, and deployment options that keep contracts and SOPs off the public web.
Evaluate like production
Groundedness checks, latency budgets, and fallbacks — so the assistant is useful on day one.
Ready to ground AI in your data?
Bring a sample of your documents. We will show you what a production RAG path looks like on a free call.
Start My RAG ProjectFAQ
RAG questions, answered
How retrieval, privacy, and timelines actually work.
What is a RAG system?
Retrieval-Augmented Generation grounds an LLM in your private knowledge base. Instead of guessing, the model retrieves relevant documents first, then answers from that context.
Which vector databases do you use?
Pinecone, Qdrant, and pgvector, chosen based on scale, cost, and whether you want the index next to your existing PostgreSQL data.
Can RAG run on confidential internal documents?
Yes. We design ingestion, access control, and deployment so contracts, wikis, and SOPs stay inside your environment while still powering accurate Q&A.
How long does a production RAG build take?
A focused knowledge assistant often ships in 3–6 weeks, including chunking, embeddings, evaluation, and a chat or search interface your team can use.