Grounded in your content, not the model's memory
General AI models know a lot about the world and nothing specific about your business. A chatbot 'trained on your data' closes that gap by retrieving your actual documents at answer time and basing its reply on them — a technique commonly called retrieval-augmented generation, or RAG.
The practical benefit: answers reflect your real policies and product details, and the bot can cite where each answer came from, so you and your users can verify it.
- Answers drawn from your PDFs, help articles, policies, spreadsheets, and web pages
- Source citations so every answer can be traced back and checked
- Updates when your documents update — no model retraining required
- Says 'I don't know' when your content doesn't cover the question
- Keeps your content within your control and access rules
'Training' vs. retrieval — what you actually need
People say 'train it on our data,' but for most businesses full fine-tuning isn't the right tool. Retrieval is cheaper, updates instantly when your documents change, and doesn't bake stale or sensitive information permanently into a model.
We recommend fine-tuning only when it genuinely helps — for example, matching a very specific format or tone — and combine it with retrieval when that's the best fit. The honest default for 'answer from our docs' is retrieval.
- Retrieval: fast to set up, always current, easy to correct — the usual best fit
- Fine-tuning: useful for style or format, but static and costlier to maintain
- We ingest and structure your content so it's searchable and accurate
- Access controls so users only see answers from content they're allowed to read
- A pipeline to keep the knowledge base in sync as your content evolves
Where a data-grounded bot fits
The same approach powers very different assistants, all sharing one trait: they answer from your material, not the open internet.
- Customer-facing help that answers from your real support docs
- Internal knowledge bots over policies, handbooks, and wikis
- Product assistants that quote spec sheets and manuals accurately
- Sales enablement that pulls from approved, current collateral
More on ai chatbots & assistants
Frequently asked questions
Is this fine-tuning or something else?
For most cases it's retrieval, not fine-tuning. The bot searches your documents and answers from what it finds, which means it's always current and can cite sources. We use actual fine-tuning only when there's a clear reason, and we'll explain the trade-off plainly.
Can it still make things up?
Grounding it in your content greatly reduces that, and citations let you verify answers — but no LLM is guaranteed error-free. We add scope limits and a 'not found in our content' response so it declines rather than guesses on things you haven't covered.
Where does our data live and who can see it?
We design around your privacy requirements. Your content can stay within your infrastructure and access controls, and for sensitive data we can use setups where it isn't used to train third-party models. We map the data flow with you before building.