Answers From Your Own Documents,With the Source Attached
Staff and customers ask a question in plain language and get the answer from your actual policies, manuals and product data — with a link to the page it came from.
- Cites its source
- Bangla + English
- Says when it doesn't know
- Live in 3–6 weeks
Grounded in your documents
Answers come from your files, not from a model's general knowledge.
Every answer cited
A link back to the document and section it was taken from.
Respects who can see what
HR sees HR files; a customer only sees what is public.
Updates when the file does
Replace the policy PDF and the answers change with it.
What is RAG (retrieval-augmented generation)?
RAG is a method for making an AI system answer from a specific set of documents instead of from general training data. When a question arrives, the system first searches your own files for the relevant passages, then asks the language model to answer using only those passages. Because the answer is built from retrieved text, it can be traced back to a source document — and the system can say it does not know when nothing relevant is found, rather than inventing a plausible answer.
A knowledge base that can prove where the answer came from
The value is not that it sounds confident. It is that a compliance officer can click through to the paragraph and check.
- Retrieval tuned to your content
- Documents are chunked and indexed around how your material is actually structured — clause-level for contracts, step-level for manuals — so the passage that gets retrieved is the one that answers the question.
- Citations on every answer
- Each response links to the document, page and section it drew from, so anyone can verify it in one click.
- Admits when it doesn't know
- If nothing relevant is retrieved it says so and offers a human contact, rather than generating a confident guess.
- Permission-aware retrieval
- Access rules are applied before retrieval, so a user can never be shown content from a document they are not entitled to read.
- Bangla and English documents
- Handles a mixed library and answers in the language the question was asked in.
What changes after launch
- ↓
- Staff stop asking colleagues what the policy says.
- 1×
- Everyone gets the answer from the current document, not an old email.
- ✓
- Every response carries the source, so nothing has to be taken on trust.
- 24/7
- The knowledge does not clock off with the person who holds it.
Fewer repeat questions
One version of the truth
Answers you can verify
Available out of hours
From a folder of PDFs to a working knowledge base
Most of the work is in the content, not the model — which is why we start there.
Swipe to see all 5 steps →
Common ways businesses use it
HR and policy helpdesk
Leave rules, benefits, conduct policy and process — answered from the current handbook.
Product and technical support
Specifications, compatibility and troubleshooting pulled from manuals and datasheets.
Contract and compliance lookup
Find the clause, the obligation and the date across hundreds of agreements.
Onboarding new staff
New joiners ask the system instead of interrupting the two people who know everything.
- Financial Services
- Healthcare & Clinics
- Manufacturing
- Education & Training
- Professional Services
- Government & NGO
- Logistics & Delivery
- Telecom & Utilities
An answer with its evidence
Overview — last 7 days
Live2,140
Documents indexed
91%
Answered with a source
6%
Declined to answer
Mon
Tue
Wed
Thu
Fri
Sat
Sun
Recent questions
HR
Handbook p.14Casual leave entitlement
Ops
Policy §7Return policy after 30 days
Legal
No source foundClause in 2019 contract
The response, the confidence, and the exact document sections it was built from — visible to the user, not hidden. Sample data shown.
Questions, answered
01How is this different from a normal AI chatbot?
A general chatbot answers from what the model learned during training. A RAG knowledge base answers only from documents you supply, and shows which ones. For policy, compliance and technical content that difference is the whole point.
02How much does a RAG system cost in Bangladesh?
It scales with the size and messiness of your document library rather than the number of users. A few hundred clean PDFs is a much smaller project than twenty years of scanned files. We quote after reviewing a sample of your content.
03How long does it take to build?
Three to six weeks for a well-defined document set. Libraries that need scanning, cleaning or de-duplication first take longer, and we will tell you that before we start rather than after.
04Can it read scanned documents and images?
Yes, using OCR — including Bangla text. Accuracy depends on scan quality, and we test a sample from your archive before committing to a timeline.
05What stops it from inventing answers?
The system only answers from passages it actually retrieved, and we set a relevance threshold below which it declines instead of guessing. That threshold is tuned with you, and every answer carries its sources so anything wrong is visible immediately.
06Can we control who sees which documents?
Yes. Permissions are applied at retrieval time, so restricted content is never even considered for a user who lacks access — it cannot leak through a cleverly worded question.
07What happens when a policy is updated?
You replace the document in its usual location and the index updates. There is no separate content to maintain, which is what stops these systems going stale after six months.
Related services
- AI Document IntelligenceExtract structured data from invoices and forms.
- AI Chatbot & Customer SupportPut these answers in front of customers.
- Custom AI AgentsAssistants that act on what the knowledge base finds.
- Bengali Language AIBuilt for a Bangla-first document library.
- Private & Local AI DeploymentKeep sensitive documents inside your own network.
- AI Analytics & Business IntelligenceAsk questions of your data, not just your documents.
See what your own documents could answer
Send us a sample of your policies or manuals and we will show you the questions a knowledge base could handle — and the ones it could not.