Document Q&A Chatbot
A document Q&A chatbot lets you upload contracts, tenders, manuals or policy files and ask questions of them in plain language. Every answer cites the specific page and clause it came from, so you can verify it in seconds rather than trusting it.
Who it's for
Legal, insurance, compliance, procurement and consulting teams.
What changes
Hours of document hunting collapse into a single question.
- Starting at
- ₹45,000
- Timeline
- 2–4 weeks
- Category
- AI Chatbots
- Built from
- Vashi, Navi Mumbai
Key takeaways
- Collapses hours of document hunting into a single question with a verifiable citation.
- Handles scanned documents through OCR, though accuracy drops on poor scans.
- Never a substitute for legal review — it finds and summarises, it does not advise.
- Builds in 3–5 weeks from ₹45,000; document volume matters more than page count.
- Confidentiality architecture is a design decision, not an afterthought.
The specific job this does
Someone needs to know whether a supplier contract has an auto-renewal clause. Today that means finding the file, opening a 40-page PDF, and reading until they find it or give up and ask the person who negotiated it.
With document Q&A they ask the question and get: yes, clause 11.3, renews automatically for twelve months unless cancelled 60 days prior — with a link to page 14. The verification takes five seconds because the citation is right there.
That is the whole product. It sounds modest until you count how many hours a week your business spends looking things up in documents it already has.
Where it genuinely earns its cost
- Contract review — obligations, renewal dates, penalty clauses, deviations from your standard template.
- Tender documents — eligibility criteria, submission requirements, and the disqualifying detail buried on page 60.
- Insurance policies — what is actually covered, exclusions, claim procedure.
- Technical manuals — field engineers asking equipment questions from a phone instead of carrying a binder.
- Regulatory and compliance files — what a specific rule requires of you, with the source.
- Historic project documentation — what was agreed with this client three years ago and why.
Scanned documents change the maths
Most Indian business paperwork has been through a scanner at some point, and that determines what is achievable.
| Document type | Extraction quality | Practical effect |
|---|---|---|
| Digital PDF with real text | Near-perfect | Answers and citations are exact |
| Clean scan, 300 dpi | High | Reliable; occasional table misreads |
| Phone photo of a printed page | Moderate | Usable; verify anything numeric |
| Old fax-quality scan | Poor | Expect gaps; flag for manual review |
| Handwritten annotations | Unreliable | Excluded unless critical and re-keyed |
We run OCR over your actual sample during the build and show you the extraction quality before you commit, rather than discovering it after go-live. Where a document scans badly, the honest answer is sometimes to re-key it rather than to pretend the system can read it.
Confidentiality, decided up front
Contracts and tenders are among the most sensitive files a business holds, so the architecture question comes before the feature question.
There are three shapes. Cloud models with an enterprise agreement that contractually prevents training on your data — fastest and highest quality. In-region hosting where data must not leave India — slower, slightly narrower model choice. Fully on-premise open models where nothing leaves your building — most restrictive, noticeably lower answer quality on complex documents.
Most clients are well served by the first. We will tell you honestly what you give up if your policy forces the third.
Why citation is non-negotiable
For document work, an uncited answer is worthless. Nobody is going to act on "the contract says you can terminate with 30 days' notice" if they cannot see where it says that.
Every answer we return carries the document name, page and quoted passage. If the retrieval finds nothing relevant, it says so instead of assembling something plausible from adjacent text — which is the specific failure that makes people abandon these tools.
What the finished workspace contains
A private workspace where you upload documents, ask questions and get cited answers; folder-level permissions; OCR for scans; bulk upload; a saved-question feature for checks you run repeatedly across new contracts; and an export of findings. Source code and deployment access transfer to you at the end.
FAQ
Document Q&A Chatbot — your questions
How many documents can it handle?
Thousands. Retrieval scales far better than reading does — the practical limits are storage cost and how cleanly the documents are organised, not the count. We have no problem indexing an entire contract archive; we do care about whether the folder structure means anything.
Can it compare two contracts?
Yes, and it is one of the most-used features. Ask what differs between this vendor's terms and your standard template and it will list the deviations with citations to both. It flags differences; a human still decides which ones matter.
What happens with tables and annexures?
Digital PDFs with real tables extract well. Scanned tables are the weakest case in the whole system — column alignment is where OCR fails most often — so we test on your actual documents during the build and tell you if tables need a different treatment.
Is our data used to train anyone's model?
No. We use enterprise tiers with contractual no-training terms, and we tell you in writing which provider processes your documents and where. If your clients' NDAs require in-country processing or full on-premise, we can build that, with an honest note about the quality difference.
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Next step
Want a Document Q&A Chatbot for your business?
Tell us what the process looks like today and we'll tell you what it would look like automated — and what it would cost.