AI agents for processes where people drown in routine.

Frequently asked questions

How is an AI agent different from a regular chatbot?

A chatbot follows a script — press 1, press 2. An AI agent understands the task in free form, remembers context, and works with the CRM, documents, and calendar through allowed tools and access rules. Critical actions — pricing, sending, signing, deletion, payments — are routed through a dedicated approval step.

Can we deploy an agent without sending data to OpenAI / Anthropic?

Yes. We use local models via Ollama, vLLM, or llama.cpp inside your perimeter. We can also connect “private” Azure OpenAI / Anthropic endpoints with no-training guarantees. The choice depends on quality and privacy requirements — at the start we map out which data can be used and which can't.

What do we need to prepare for the pilot?

Minimum: a description of the process, 2–3 examples of real input data (without personal info, if it can't be shared), a list of systems to work with, and a definition of success — for example “reduce request processing time from 30 to 5 minutes” or “close 60% of routine support questions”.

How do you verify the agent's answer quality?

We log every action and build an evals dataset of questions and expected answers during the pilot. We re-run evals to catch regressions early — before the client notices. For critical logic we add a dedicated approval step before actions and guardrails (filters for forbidden topics and outputs).

What happens if the agent isn't sure about an answer?

The agent is designed so that on low confidence (from logs/self-check) or when going outside allowed topics — it hands the case off to a human with full context: what was asked, what it found, what's missing. No “make up something plausible” — that's a baseline requirement for a B2B agent.

Can we start with one process and expand later?

Yes — that's the default playbook. The pilot is one agent, one process, one department. After that we either expand scenarios, plug into adjacent processes, or add a second agent alongside. The architecture is built for expansion from day one: shared memory layer, shared integrations, shared log bus.

Which platforms and models do you build agents on?

We don't lock in one. We pick the stack based on the task, data, and security requirements. Models: OpenAI / GPT, Anthropic / Claude, Google Gemini, DeepSeek, Grok / xAI, GigaChat, YandexGPT, or local open-source models. Agent logic: LangGraph, LangChain, LlamaIndex, custom workflows; Dify — for fast low-code prototypes when appropriate. Integrations: Telegram, WhatsApp, VK Teams, Pachca, Yandex 360, Google Workspace, Microsoft 365, Bitrix24, amoCRM, 1C, Kontur, MoySklad, REST API, webhooks, MCP, n8n, Make. When you need a quick pilot — we take the shortest path. When you need production, privacy, and control — we build a custom contour.

How is an agent different from RPA / n8n / Make?

RPA and no-code tools like n8n or Make work great on rigid scenarios: “if A happened — do B.” An AI agent is needed where the scenario can't be fully written in advance: incoming items vary, data is incomplete, you need to read documents, compare options, take context into account, and pick the next step. In practice we often combine both approaches: n8n or a backend service handles the reliable mechanics of the process, while the agent acts as the reasoning layer — it understands the task, picks the route, prepares the output, and hands critical actions to a human for approval.