Chatbots

What the service includes

We design and build chatbots for sales and support: we work out the dialogue scenarios, write the request-handling logic, connect the client's data sources and roll the bot out to Telegram, the website or messengers. First we analyze the client's real inquiries, isolate recurring questions and deals, then turn them into a tree of scenarios and a set of answers based on your data rather than abstract templates. We connect the bot to the CRM so that leads and conversation history land in cards automatically, without manual transfer. If the loop includes the client's equipment, we write the software for it and set up data exchange, but the device itself stays yours and runs on your side. In the end you get a working system with documentation on the scenarios and access, not a one-off script that no one can maintain.

How it really works

The bot receives a message, determines the user's intent and picks a branch: answer from a predefined scenario, pull data from the database or hand the question to a language model. Simple, frequent cases are handled by hard rules because they're predictable and cheaper to operate, while free-form wording and non-standard requests go to the LLM with a predefined context about your product. Between these layers stands an orchestrator on n8n that connects the Telegram API, the knowledge base, the CRM and the model into a single flow and decides where to route each request. All inquiries are logged, so you can see where the bot answered on its own, where it handed off to an operator and where it made a mistake, and we use this data to refine the scenarios. This scheme gives control over the answers: chatbots don't make things up where there's an exact rule, and bring in the model only where it's actually needed.

Where chatbots came from

The first program of this kind is considered to be ELIZA, which Joseph Weizenbaum created at the Massachusetts Institute of Technology in 1966; its DOCTOR script imitated a psychotherapist's replies by substituting templates, even though the machine didn't understand the meaning of the dialogue. In 1972 the psychiatrist Kenneth Colby wrote PARRY, which modeled a patient's speech, and in 1995 Richard Wallace released A.L.I.C.E. with a large base of "pattern — response" rules. In 2001 SmarterChild appeared from ActiveBuddy, which ran right inside AOL Instant Messenger and MSN Messenger and was the first to bring chatbots into mass-market messengers. All these systems remained scripted: they matched patterns and didn't build answers by meaning. The turning point came with language models: the launch of ChatGPT on 30 November 2022 made generative answers available to the broad market and set the current format of the service.

Why precise setup matters

A bot speaks on behalf of the company, so the cost of an error in the logic is a lost lead or a wrong answer to a client, not just a code crash. A language model without hard guardrails tends to invent facts, prices and terms, and this is fixed not by magic but by engineering: precise scenarios, checking answers against the database and limiting the model to allowed context only. The quality of the result is set not by the mere fact of plugging in a neural network but by the care of the assembly — how intents are described, how branches are separated, how empty and ambiguous cases are handled. The same principle applies when we prepare a program for a client's equipment: a machine cuts, welds or prints by a control program, and the result on the metal depends on its correctness, not on a pretty interface. That's why the main work lies in the software part and its debugging, not in plugging a ready-made API in a couple of clicks.

Our stack and tools

We build the bot's framework in the messenger on the Telegram Bot API — this is the official interface for receiving messages, sending answers, buttons and webhooks. The connecting link is n8n, an open automation platform where a flow is assembled from nodes: a request came in, we checked the database, called the model if needed, wrote the result to the CRM. The LLM is responsible for free-form dialogue and understanding wording; we give it context about the product and rules of behavior so that answers rely on your data, not on general information from training. CRM integrations (transferring leads, statuses and conversation history) close the loop so that conversations turn into accounted-for deals rather than staying in the chat. We pick the stack to fit the task: if the client already has equipment or their own system, we write the software around it and set up the exchange rather than forcing a replacement.

When the key tools appeared

The Telegram Bot API was opened on 24 June 2015, and it was from that moment that it became possible to build chatbots at scale right inside the messenger without separate apps. The current wave of language models was launched by Google's paper "Attention Is All You Need" in 2017, which described the transformer architecture that today's LLMs are built on. Based on it, OpenAI released GPT-1 in 2018 and GPT-3 with 175 billion parameters on 28 May 2020, while the public ChatGPT came out on 30 November 2022. The automation platform n8n, which we use as an orchestrator, appeared as an open project: its first version was published on GitHub on 23 June 2019 by Jan Oberhauser in Berlin. These dates show that the entire working stack came together literally over the past decade, and applying it well today is a separate engineering competency.

Why you can trust this to us

The combined experience of the development team is more than 45 years in IT, and that's not a marketing line but accumulated lessons on integrations, error queues and supporting systems in production. We approach a bot as an engineering task: we fix the requirements, analyze the client's data, design the scenarios and check the behavior on a staging environment before the production launch, rather than rolling something raw into a live chat. Before the start we run typical and edge-case scenarios, look at the logs and close off the places where the model could give an inaccuracy. We honestly keep our role's boundary: we write programs and configure the client's equipment that cuts, welds, prints or hauls on their side, and we're responsible for the software and the setup, not for what we physically don't do. This approach yields a predictable result and a system that can then be developed rather than rewritten from scratch.