Two extremes that annoy customers
Old scripted bots march people through “press 1” menus and cannot parse a real question. A bare ChatGPT is the opposite: it sounds smooth but does not know your product and confidently invents things — terms, prices and features that do not exist. Both end the same way, with an irritated customer asking for a human.
The setup that works: RAG plus honest escalation
A support bot worth deploying answers from your knowledge base: documentation, procedures, the history of resolved tickets. Technically that is RAG — the bot first retrieves the relevant passage from your material, then answers from it rather than from memory. The mechanics are covered in how to train an AI on your own data.
What it takes off your team
| Support problem | What the bot does |
|---|---|
| Customers wait hours for a reply | answers instantly, around the clock |
| 70% of tickets are routine | closes them from the documentation itself |
| Knowledge lives in people’s heads | answers the way your best specialist would |
| A genuinely hard case | escalates to a human with the full conversation history |
Where it runs and what happens to the data
- •Channels: a website widget, Telegram, email — all sharing one knowledge base,
- •Guides the customer to the next step: a request, a payment, a booking,
- •Shows what people ask about most, which is free product research,
- •Conversations contain personal data, so processing runs on our infrastructure or on your own server.
More about the product on the AI Support page.
Frequently asked questions
How do you stop a bot from making things up?
It has to answer from your knowledge base through RAG rather than from general knowledge, and it has to say “I don’t know” when the answer is not in the material. That rule is enforced strictly: no source, no answer — the question goes to a person instead.
How fast can it launch?
If the documentation already exists, three to seven days. Most of that time goes not into the technology but into gathering and tidying the material the bot will answer from.
What happens with complex tickets?
It does not try to solve them. It escalates, handing the specialist the conversation history along with its own reading of the problem, so the human starts with context instead of from scratch.