AI Integration · RAG on your documents

Ask any question about
your document.

Drop a PDF, Word doc, or text file and ask questions about it. Every answer is grounded in the file itself with the exact lines quoted back. That is real retrieval-augmented generation (RAG), never fabricated. Same pattern scales into a production build wired to your contract repository, knowledge base, or intranet.

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AI Document Q&A

Drop a document, ask any question, get answers with the exact lines quoted back.

,

Ask a question

The Impact

What AI-integrated document
assistants unlock for your team.

Tell us your document stack

Answers, with quotes

Every answer cites the exact passage it came from. If the document doesn't cover it, the AI says so. It never fabricates.

Multilingual out of the box

Documents in any major language: contracts, policies, manuals, RFPs. Mixed-language repositories work too.

Hours saved per week, per team

Legal, ops, sales, and support teams stop skimming 80-page PDFs. Ask the question, get the citation, move on.

Your model, your storage, your framework

Leading enterprise AI providers or self-hosted open-weights models. Any modern vector store. Your auth, your audit log.

Inside this build

The integration layer that makes RAG usable on real documents.
The same building blocks power yours.

Every component below is what a production document-AI build needs. The model is a commodity; the value is in the pipeline wrapped around it: ingestion, chunking, embeddings, retrieval, grounded answers with citations.

Real document understanding

The AI doesn't skim. It reads the whole file and finds the passage that answers your specific question, even when the wording differs.

Grounded answers, with quotes

Every answer comes from your document. The AI quotes the exact line(s) it used. If the answer isn't in there, it says so. It won't fabricate.

Ask anything specific

"What is the renewal clause?" "What is the total cost?" "Which section covers data retention?" Get a direct answer instead of skimming 80 pages.

Private by default

The document is held in memory for an hour so you can ask follow-ups, then dropped automatically. Nothing written to disk; nothing logged. Custom builds support self-hosted models and zero data retention.

PDF, Word, text

Drop in the file the team actually has. We extract the text automatically. Up to 8 MB. (Scanned image PDFs need OCR. A custom build adds it.)

Built to wire into your framework

For a real deployment we plug it into your contract repository, knowledge base, or wiki. Your auth, your storage, your model choice: including self-hosted open-weights models if your data can't leave the building.

How we build it

4 steps from a document
to grounded answers with citations.

The RAG pipeline the demo runs. The same pipeline scales into a production build wired to your document store, with auth, audit logs, and evaluation harnesses layered on.

01

Upload your document

PDF, Word, or plain text up to 8 MB. The AI reads it in seconds. No setup, no login.

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02

The AI reads it

The whole document gets prepared so the AI can find the parts that match a question, even when your wording differs from the document's.

03

You ask anything

The AI finds the parts of the document most relevant to your question and uses only those parts to answer.

04

Grounded answer back

The answer cites the exact lines used. Keep asking follow-ups. Your document stays loaded for an hour.

Frequently Asked Questions

Is this a product we can subscribe to?
No. What you see here is a live demo of a RAG pipeline. A production build is delivered as software you own, running inside your infrastructure, wired to your actual document store. No monthly seat fees, no vendor lock on where your files sit.
How reliable are the answers?
Every answer is grounded in the file you uploaded, with the exact lines quoted back. If the document does not cover something, the model says so plainly instead of guessing. Nothing hallucinated; nothing added from the model's own knowledge.
Does it work outside English?
Yes. English variants (US, UK, Indian), Hindi, Spanish, French, German, Portuguese, Arabic, Mandarin, and most major European and Asian languages all work. Ask in one language, the answer comes back in the same language. Mixed-language repositories (an English contract with a Spanish addendum, say) get handled naturally.
What is RAG, technically?
Retrieval-augmented generation. Three stages: chunk the document into passages, embed each chunk into a semantic vector, retrieve the top-k chunks most similar to the question, then send just those chunks to the model with the question. Answer is grounded in retrieved passages, not in the model's training data. Prevents hallucination and keeps the answer traceable back to the source.
Which formats work?
PDF, DOCX, and plain text up to 8 MB. The PDF needs real selectable text (not a scan). Scanned PDFs need OCR first; production builds add that plus table extraction and chart-to-text for image-heavy documents.
Where does the document go?
For the demo: file + embeddings sit in server memory for 1 hour so follow-up questions work, then drop automatically. Nothing lands on disk, nothing lands in a DB. For production, files stay entirely inside your infrastructure: your object storage, your vector database, whatever you already run.
What does a real document-AI integration look like?
For legal: wired to the contract repository. Any contract is one click from being asked questions; every answer gets an audit log for compliance. For ops: over runbooks and SOPs, exposed as a Slack bot the team asks in-channel. For sales: over the product knowledge base + spec sheets, embedded inside the CRM. Same RAG shape, different data source and different consumption surface.
How long until it goes live?
A first working slice, over one document type on your infrastructure, typically goes live once scope is agreed. Adding more document types, deeper ingestion (OCR, table extraction), auth integration, per-team scoping, and audit logging layers on once you have proof from real questions.
Is our content used to train AI?
No. Demo hits an enterprise API whose terms exclude your inputs from training. For production you pick: enterprise API with zero-retention contract, or a self-hosted open-weights model running fully inside your VPC so document text never crosses a vendor boundary.
Which document types benefit most?
Structured prose: contracts, policies, manuals, reports, RFPs, research papers, technical specs, internal wikis, exported knowledge bases. Spreadsheet-heavy or image-heavy files need extra pipeline work first (table parsing, OCR, chart-to-text). All handled in a production build.
How is this different from ChatPDF or LangChain?
ChatPDF is a hosted product: your documents live on their infrastructure. LangChain and LlamaIndex are libraries you would still need to compose into a real application. We build the production application end-to-end on your framework: ingestion, chunking, embeddings, vector store, retrieval, chat UI, evaluation harness, audit log, and auth integration. You own the code.
Can Entexis build this for us?
Yes. This is what we do. Hire us on Upwork and we will scope your first document type, wire the RAG pipeline into your document store, pick the right model + vector store for your compliance bar, and get a working first slice live once scope is agreed. You own the code, the model runs where you choose, and your files stay on your infrastructure. Look for us at upwork.com/agencies/entexis.
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AI-Integrated Document Q&A · Production-ready

Custom document AI, wired
into your framework.

The demo above is one shape. We also build contract assistants over legal repositories, policy lookups for compliance teams, SOP agents for ops, and knowledge-base chatbots for support. First working slice quickly, on your infrastructure, with your files staying on your side.

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