RAG Knowledge Base Development
Answers from your own documents, with the source cited every time.
- From
- ₹1,25,000
- Timeline
- 3–5 weeks
Companies accumulate thousands of pages — SOPs, contracts, product specs, past tickets — and none of it is findable at the moment someone needs it. New staff ask colleagues instead of reading, and the answers drift.
A retrieval system fixes that: ask in plain language, get an answer assembled from the actual documents, with a citation you can click to verify.
What we build
- Document ingestion from Drive, SharePoint, Notion, Confluence, S3 or a folder
- Chunking and embedding strategy tuned to your document types
- Vector database setup — Qdrant, pgvector, Pinecone or Weaviate
- Hybrid keyword-plus-semantic search for better recall on names and codes
- Citations on every answer, linked to the source passage
- Permission-aware retrieval so people only see what they're entitled to
- Automatic re-indexing when source documents change
- Evaluation set to measure answer quality objectively
Examples of this work
Concrete builds rather than capability statements.
Internal SOP assistant
500+ operational documents made queryable in Slack, cutting the 'who knows how to do this' interruptions that were eating senior staff time.
Contract query tool
Lets the commercial team ask about renewal dates, penalty clauses and payment terms across several hundred agreements, with the clause quoted and linked.
What you get
- Indexed knowledge base with a documented refresh pipeline
- Chat interface on web, Slack or WhatsApp
- Citation and permission model
- Quality evaluation report
Tools we use here
- Qdrant
- pgvector
- OpenAI
- Claude
- n8n
- Google Drive
- Notion
RAG knowledge bases — questions we get asked
How many documents can it handle?
Tens of thousands without difficulty. Quality depends far more on how well documents are structured and chunked than on raw count — messy scanned PDFs need more preparation than clean text.
Can it respect our access permissions?
Yes. We attach access metadata at index time and filter retrieval by the asking user's groups, so a search never surfaces a document the person couldn't open directly.
Want RAG knowledge bases working in your business?
Book a free 30-minute audit. We'll tell you what it would take, what it would cost, and whether it's worth doing at all.
No obligation · Reply within 1 business day · NDA on request