RAG Chatbot & Knowledge Assistant Development
Answers grounded in your documents — accurate, cited, current.
Retrieval-Augmented Generation (RAG) lets an AI assistant answer from your own private knowledge — PDFs, wikis, SharePoint, Confluence, tickets, contracts and databases — instead of guessing. We build RAG systems that retrieve the most relevant passages, generate a precise answer, and cite the exact source so your team can trust and verify it.
Our RAG pipelines handle the hard parts: high-quality chunking, hybrid semantic + keyword search, re-ranking, freshness, and role-based access control so users only see what they're allowed to. The result is a knowledge assistant that stays accurate as your documentation grows and changes.
₹2,75,000
one-time · 4–7 weeks
₹28,000/mo
hosting, monitoring & tuning
Why teams choose this
Grounded, cited answers
Every response links back to the source document, so answers are verifiable and audit-ready.
Always current
Connect live data sources — new documents are searchable within minutes, no retraining.
Secure & access-aware
Row-level and role-based permissions ensure users retrieve only what they're entitled to.
Cuts research time
Turn hours of manual document search into a five-second, precise answer.
Our proven process
Source audit & ingestion
We inventory your content sources and build secure, incremental ingestion pipelines.
Chunk, embed & index
We tune chunking and embeddings, then index into a production vector store.
Retrieve, re-rank & generate
Hybrid search plus re-ranking feeds the LLM the right context for grounded answers.
Evaluate & harden
We measure answer accuracy with an eval suite and add guardrails before launch.
Modern, best-in-class stack
RAG Chatbots services near you
Common questions
It's an AI assistant that first searches your own documents for relevant information, then writes an answer based only on what it found — and shows you the sources. This keeps answers accurate and specific to your business.
We constrain the model to answer from retrieved context, add citation requirements, set 'I don't know' fallbacks, and run an automated evaluation suite that scores factual grounding before and after launch.
Yes. We enforce role- and row-level access at retrieval time so each user only gets answers from documents they're authorized to see.
PDFs, Word, websites, Notion, Confluence, SharePoint, Google Drive, databases, Zendesk and more. We build connectors that keep the index fresh automatically.
Explore what pairs well
Let's build what's next.
A free 30-minute consult. We'll map your highest-ROI AI use case and show you exactly how we'd ship it.