A chatbot is useful where it closes the question, not where it answers it.
Most bots annoy people because all they can do is talk: the user gets text and goes to a human anyway. We build bots wired to your data and systems, that take a routine request to a result and hand everything else over honestly.
Answers from your data
The bot answers from your knowledge base and documents, not from the model's general knowledge. Each answer shows its source so a human can verify it.
Actions, not just replies
Check an order status, book an appointment, file a request, gather data for a human — the bot works through tools wired into your systems.
An honest handover
The bot has to be able to say “I don't know” and pass the conversation to an operator with context. That matters more than another five per cent of scenario coverage.
Channels and integrations
Website, Telegram, internal interfaces. One logic, several channels — no need to rewrite the bot for each of them.
How is this different from an AI agent?
By the boundary of responsibility. A bot holds a dialogue with a person and acts within it. An agent works inside your processes and can act with no dialogue at all.
Will the bot make answers up?
The risk is reduced by grounding answers in your documents through retrieval instead of free generation, plus guardrails and quality evaluation on your scenarios. It can't be eliminated — which is why on sensitive topics the bot hands over to a human.
Do we need a ready knowledge base?
Desirable but not required. Often the first step is collecting and tidying what already exists in documents and correspondence. That's separate work and it's better not hidden inside the bot's estimate.
How long does launch take?
It depends on how many scenarios we cover and how accessible the systems are. Concrete timelines come after discovery.
Off-the-shelf chatbot or custom: counting the cost of ownership
An off-the-shelf bot launches in a week and looks ten times cheaper. The difference shows up not in month one but where the bot has to look inside your systems, answer from your documents and hand over to a human honestly. What the two-year bill is made of and how to choose without overpaying either way.
How to build an AI agent: what you need beyond the prompt
The prompt and the model are the smaller part of the job. An agent becomes usable when it has tools, explicit authority boundaries, state, a log and cost control. A step-by-step breakdown of what it is made of, how to get it to production and where it breaks.
Checklist: are your documents ready for RAG
PDF, 2 pages. No sign-up, no email — just the file.