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 does not, the AI says so plainly. It will not invent facts to fill gaps.

How does retrieval-augmented generation (RAG) 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 is 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 will not work. Those need OCR first.

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 do not 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 is 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 + product specs. The result lives on your stack with your choice of AI provider and your choice of vector store.

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 goes live once scope is agreed. 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 my 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 deliver 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 with this RAG demo?

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).

AI Integration · RAG on your documents

Ask any question about your document.
Upload it. Get answers with the exact lines quoted back.

Q: 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 would 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.

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. This is real retrieval-augmented generation (RAG): the model reads the document, embeds it into semantic vectors, retrieves the most relevant passages per question, and answers only from what is there. The same pattern scales into a production build wired to your contract repository, knowledge base, or intranet.

Live demo. Multilingual out of the box (English, Hindi, Spanish, French, and more). Uploaded files live in memory for 1 hour so follow-up questions work, then drop automatically. Nothing is written to disk.

Drop a document, or click to choose

PDF, Word (.docx), or plain text, up to 8 MB. Works in any major language (English, Hindi, Spanish, French & more).

Choose file

Ask a question

Where this leads

What AI-integrated document assistants unlock.

The outcomes teams see once a RAG pipeline lives inside the systems they already run. Exact numbers vary by document type and volume, but the shape of the gain is consistent.

Grounded

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.

10+ languages

Multilingual out of the box (English, Hindi, Spanish, French & more)

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

10-40 hrs

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 stack

Your model, your storage

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

1 slice

Where every build starts

Basic version tuned to your document types goes live once scope is agreed. Then we layer on integrations, audit logs, and multi-tenant access.

Under the hood

The integration layer that makes RAG usable on real documents.

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 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 open-weights models if your data can't leave the building.

Multilingual (English, Hindi, Spanish, French & more)

Documents in any major language: Hindi, Spanish, French, German, Arabic, Mandarin, Portuguese. Mixed-language repositories work too. Answers come back in your question's language.

The build path

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.

Upload your document

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

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.

You ask anything

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

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 stack: 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.