Answers from your data — not from the internet.
Most chatbots guess. Yours will not: it answers from your own documents, calls your systems when a number is needed, and stays silent rather than inventing. Every conversation ends as a contact, not a dead end.
Teams drowning in the same twenty questions. Teams whose support answers live in ten different documents. Teams that tried an off-the-shelf bot and got confident nonsense.
Not a fit if you only need a contact form — a bot is the wrong tool for that.
Knowledge Base
Your documents, pages and price lists collected, parsed and indexed so the bot retrieves the right passage — and re-indexed when content changes.
Agent & Tools
The engine: sessions, conversation history, streaming, usage limits and cost control. Integrations into your systems are priced separately.
Widget & Lead Capture
The bot on your site, with lead capture that pushes the contact straight into CRM instead of letting the conversation end nowhere.
Answer Reliability
Evidence gating, dialogue logs, quality review and correction — the part that separates a working bot from a demo.
How much will it cost to build an AI chatbot in 2026?
Off-the-shelf widgets run $50–500 a month and answer from a knowledge base you paste in. A custom bot that answers from your systems and takes actions starts at €5,950 to build, plus model tokens as a running cost — like hosting. The gap is not conversation quality. It is whether the bot can do anything beyond talking.
Can we train a chatbot with your own data?
Not train — retrieve. Training a model on your documents is expensive, slow, and goes stale the day your prices change. What works is retrieval: documents are indexed, the relevant passage is found at the moment of the question, and the model answers from that text. Update a document and the bot is current the same day, with no retraining.
Is there a way to stop AI from hallucinating?
You cannot stop a model from generating, but you can stop the output from leaving. The rule that works: every answer must cite a retrieved passage, and an answer without one is blocked rather than shown. Prompt instructions alone — "do not make things up" — fail, because the model has no way of knowing that it is.
Why do RAG systems fail?
Almost always retrieval, not the model. Documents split in the wrong places, so the answer sits across two chunks and neither is retrieved whole. No refusal path, so when nothing relevant is found the model answers anyway. And an index that is never rebuilt, so the bot confidently quotes last year’s prices.
Is an AI agent the same as a chatbot?
A chatbot answers. An agent acts — it checks stock, calculates a price, books a slot, files a request, by calling your systems. From outside the interface looks identical. The difference is whether it can only tell you something or actually do it, and that difference is most of the build cost.