A chatbot that answers from your documents — and never makes things up

This is a common misconception: you don't train a chatbot on your own data, you let it search through it. Retraining a language model (fine-tuning) on your documents is expensive, slow, has to be redone on every change, and the model still invents things. The better approach is called RAG (retrieval-augmented generation): your documents are indexed, and on every question the system retrieves the right passages and hands them to the model as context. That way it answers from current sources with citations, and you update your knowledge base simply by replacing a document — no retraining needed. TalkWithData works entirely on this retrieval model.

The risk of fabrication is sharply reduced by grounding: the model may only answer from the passages retrieval pulled up, not from its own memory. If it finds nothing relevant, it says "I don't have that information" instead of guessing. Every answer also shows its source, so you can verify it in one click. There are no absolute guarantees in AI, but verifiability and openly admitting ignorance are what separate a demo from something you'd actually build on.

PDF, Word, Excel and PowerPoint upload directly. On top of that you connect sources like Google Drive, Dropbox and OneDrive, websites, databases and APIs. During indexing each document is split into passages and enriched: the system prepends a short context sentence and generates a few hypothetical questions that improve findability, so the chatbot retrieves your documents even when you phrase things differently than the text does.

Everything is EU-hosted: vectors in ChromaDB, metadata and access rights in PostgreSQL, both inside the EU. Every query is isolated per organisation and department, so one department's data never surfaces in an answer for another. Your data is not used to train external models. GDPR compliance and EU hosting are our baseline, not an afterthought — but the real difference is in what you can inspect and verify.

There are four ways, and you can deploy the same knowledge base through all of them at once. A chatbot widget on your website or intranet; a browser extension that injects the knowledge into the AI tool your team already uses; an MCP server that lets ChatGPT, Claude and others call your knowledge base as a tool; and an API for your own product. You build the knowledge base once and choose per situation how to serve it.

Yes, and this is exactly where purely semantic chatbots break down. Semantic search finds meaning but treats "article 6(1)(b)" or a precise product code as vague noise. That is why TalkWithData runs hybrid: the lexical BM25 component catches exact textual matches, the semantic component catches intent, and Reciprocal Rank Fusion merges the two. So you get both the literal match and the contextually correct answer at the top.