AI practice · Gurugram
RAG chatbot development for company knowledge — not another generic bot
We build retrieval-augmented assistants that answer from your PDFs, help centre, tickets, and policies — with citations your team can trust, and a human path when the model should not guess.
A RAG chatbot is useful when your answers already exist in documents, CRMs, or support history — and people waste hours hunting for them. We do not start with a model name. We start with the questions your staff or customers ask every day, the corpus you actually have, and the channel they already open: web, in-app, or WhatsApp.
Retrieval, chunking, access rules, and evaluation are the product — not a chat widget bolted onto a homepage. The same Gurugram studio can ship the website or app around the assistant so it feels native, not like a sidecar vendor.
Who this is for
Operations, support, and professional-services teams that answer the same questions from handbooks and tickets; product companies that want in-product help grounded in their own data; and buyers who need private retrieval rather than a public chatbot that invents answers.
Problems we solve
Staff cannot find the latest policy
Assistants retrieve from the approved corpus and show sources, so people stop asking the same Slack question twice.
Support repeats the same answers
Deflection on web or WhatsApp with a clear handoff when confidence is low or the ticket needs a human.
Knowledge is locked in PDFs and drives
We index what you allow, respect roles where needed, and keep a path to refresh the index when content changes.
A demo chatbot that cannot go to production
We design for logging, evaluation, fallbacks, and the UI people will actually use — not a slide-deck prototype.
Where a private knowledge assistant fits
Internal policy and ops handbooks
Staff ask how leave, expense, or escalation rules work — and get answers with links to the approved source, not a Slack guess.
Customer and partner support
Deflect repeat questions on web, in-app help, or WhatsApp from product docs and tickets, with a clear human handoff.
Technical documentation and onboarding
Engineers and new hires query manuals, release notes, and SOPs without opening ten PDFs.
Document ingestion, retrieval, and deployment
We inventory sources (PDF, CMS, drive, tickets), clean and chunk them for retrieval, and build an index you can refresh when content changes. Hybrid search and re-ranking are used when keyword-only retrieval misses the real answer.
Access control follows your roles where the product requires it. Deployment can use hosted models under your data terms, or a private path when documents must not leave your boundary — decided in discovery, not as a surprise at launch.
Citations, refusal behaviour, and a small evaluation set of real questions are part of the build so you can see grounded answers versus “I don’t know” before you open the assistant to a wider group.
What you get
A production assistant on a defined corpus, with the integrations and guardrails required to run it after launch.
- Corpus audit and chunking strategy
- Retrieval index and access rules
- Web, in-app, or WhatsApp channel
- Citations and refusal behaviour
- Human fallback and escalation
- Evaluation set and monitoring hooks
- Admin path to refresh content
- Documentation for your operators
Security, testing, and maintenance
We agree store, vendor, retention, and whether data may leave your VPC before production traffic. NDAs are standard. Private retrieval is the default when the corpus is sensitive — we will not train a public model on your documents unless you ask for that in writing.
We test with real questions from your team, measure grounded answers versus refusals, and check that the assistant fails closed when retrieval is weak. Product QA in the same studio covers the screens and handoffs around the chat.
Questions about rag chatbots
What is RAG chatbot development?
RAG (retrieval-augmented generation) means the assistant searches your approved documents before answering, and can cite what it used. It is usually faster and safer to production than training a custom model on day one.
Will our documents train a public model?
Not unless you ask for that in writing. We agree store, vendor, and retention before production traffic.
Can this run inside our existing product?
Yes. Many engagements put the assistant on an existing website, help centre, or mobile app. See our in-product AI assistant work study for how that looks in practice.
When do you recommend fine-tuning instead?
When retrieval alone cannot capture tone or specialised behaviour and you have clean training data. We explain the trade-off before you pay for either path.
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Workflow automation
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Agents that take steps in your tools — with approvals — when chat alone is not enough.
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Primary practice
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RAG, chatbots, NLP, and ML inside the product — the broader AI practice.
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Product surfaces
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Portals and apps where the assistant lives next to the work.
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Startup products
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When the assistant is part of a new product you need to ship.