Entexis Labs · live demo of an AI document Q&A (RAG) Want one for your team? →
Entexis Labs · Live RAG Demo

AI Document Q&A.

Drop a PDF, Word doc, or text file and ask questions about it. Real retrieval-augmented generation (RAG) — the AI reads your document, finds the relevant passages, and answers grounded in your file. Built end-to-end by Entexis.

This is a working prototype. Try it live below — or read on for how an AI document assistant replaces hours of manual reading.

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Ask a question

What it does

Hours of reading, in seconds.

A real document assistant — built so the AI answers only from the file you uploaded, with quotes. Same pattern Entexis ships into client stacks for legal, ops, sales, and support teams.

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 stack

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 Llama or Mistral if your data can't leave the building.

How it works

RAG, made tangible.

Drop a document, ask any question, and watch the answer come back with the exact lines it used. The same retrieval-augmented generation pattern that Entexis ships into production for client document stores.

01

Upload your document

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

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

How accurate is the RAG answer?
Answers come from your document only. If the document covers it, the answer is grounded and quotable. If it doesn't, the AI says so plainly. It will not invent facts to fill gaps.
How does the RAG pipeline work here?
Three steps, in plain language. First, the AI reads your document. Second, you ask a question. Third, the AI finds the parts of the document that match your question and answers using only those parts — that's what makes the answers grounded instead of guessed.
What file formats can I upload?
PDF, Word document (.docx), and plain text up to 8 MB. The PDF must contain real text (you can highlight and copy from it). Scanned PDFs that are just images won't work — those need OCR first, which a custom build handles.
Is my document stored?
No. The document and its embeddings are held in memory only for an hour so you can keep asking follow-up questions, then dropped automatically. Nothing is written to disk and we don't log document content. For a custom build, your data stays on your stack — including options with zero data retention and fully self-hosted private models.
Can Entexis build a custom RAG application for our team?
Yes — that's the day job. For a legal team we wire it into the contract repository so any contract is one click from being asked questions, with answer audit logs for compliance. For operations we wire it into runbooks and SOPs. For sales we wire it into your knowledge base and product specs. The result lives on your stack with your choice of model (OpenAI, Anthropic, self-hosted Llama, Mistral) and your choice of vector store (in-process, Postgres pgvector, Pinecone, Weaviate).
How long does a custom RAG build take?
The basic version of what you see on this page, tuned to your document types and hosted on your stack, typically ships in about two weeks. Wiring it into a document store, an authentication system, an audit log for citations, or a multi-tenant setup for different teams adds scope from there.
Will our document content be used to train AI models?
No. We use enterprise-grade AI partners whose policies state that content sent through their interface is not used to train models. For a custom build, we ship with whichever AI partner your team prefers — including options with zero data retention, contractual no-training guarantees, or fully self-hosted private models so document content never leaves your environment.
What kinds of documents work best?
Anything with structured prose — contracts, policies, manuals, reports, RFPs, research papers, technical specs, internal wikis, knowledge base exports. Spreadsheets and image-heavy documents work less well in the public demo; for those, a custom build extracts the structure first (table parsing, chart-to-text, OCR for scans).
How is this different from ChatPDF or LangChain?
ChatPDF is a hosted product — your document goes to their stack. LangChain and LlamaIndex are libraries you'd still need to wire into a working application. We build the production application end-to-end on your stack: ingestion, embeddings, vector store, retrieval, the chat UI, evaluation harness, audit log, and integration with your auth and document repositories. You own the code.
Entexis Labs · Production-ready

Want a RAG application like this for your business?

We build the document Q&A you actually need — wired into your contracts, knowledge base, or wiki. Your model, your storage, your data boundaries. Basic version typically ships in about two weeks.

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