AI integration and chatbot development that does real workAI integration and chatbot development that does real work

There is no single number, because a “Telegram bot” can be a simple notification menu or a tool that takes orders, reads live stock and tracks payments. Cost is driven by how many of your systems it touches, whether you need an admin panel, and whether an LLM is involved. We recommend starting with one well-defined job: a small bot that works reliably earns its keep quickly and can grow from there. For a real estimate, we walk through your current process together in a short call.

If you only answer generic, stable questions, a hosted chatbot is usually enough and cheaper. Once the bot needs live data — prices, stock, order status, customer accounts — or has to take real actions in your system, off-the-shelf tools hit a wall. A custom bot keeps your data, business rules and model costs under your control. Sometimes the right answer is both: a hosted widget for FAQs and a custom bot for operations. We will tell you honestly which fits.

Yes — most of our AI work is exactly that. The model connects to your system through an API; if you don’t have one, we add a small NestJS layer that exposes scoped access to your database or services. We use structured output so responses can be validated before anything is written, cache repeat requests to control cost, and can fall back between providers or to a local Ollama model. Your current site or app stays as it is; the AI layer sits beside it.

Modern large models understand both well, but they still slip on Persian digits, the solar calendar, half-spaces, proper names and colloquial phrasing. So we never trust the model blindly: output is constrained to a schema, numbers and dates are validated in code, and sensitive actions sit behind a confirmation step. That approach is what lets Paradise Desk users enter transactions as ordinary Persian sentences and get correct records. With those guardrails, language quality stops being the risk.

With hosted APIs such as OpenAI or Gemini, prompts are sent to the provider’s servers, and we review each provider’s data-retention terms with you before starting. For sensitive data there are three options: mask personal information before sending, send only the minimum the task needs, or run a local model with Ollama on your own server so data never leaves your infrastructure. Local models give more control but trade off answer quality and hardware cost, which we size honestly.

Architecturally they are close: both offer a bot API and can receive messages through webhooks or long polling. The differences are in API details, limits and audience. We usually write the bot’s logic once and add a thin adapter per messenger, so one codebase serves both channels. In Hamdor, the Bale bot runs inside the API process over long polling, so there is no separate service to operate. If your users are on both messengers, building for both at once is sensible.