AI agents that get the work done
We build agents for CRM, tickets, documents, WhatsApp, Telegram and internal tools. The agent pulls the data, prepares the action, and writes down what it did. The cases that need a person go to a person, with context attached
What an AI agent can take off your team’s plate
A good first workflow repeats every day, already has usable data, and ends with a result the team can check
A message, form, or ad lead arrives with missing fields.
The agent asks for missing details and prepares a clean CRM record.
A manager gets a ready lead instead of a raw chat.
AI agents already at work
Not concepts or demo prompts. These systems work with real candidates, customers, and operational data
Magnum HR Agent
The agent screens candidates, finds the nearest location, and books the next step across a network of hundreds of stores.
Almaty Marathon
The agent handled about 2,130 participant conversations and sent the remaining cases to support with context.
AI CRM
The agent answers from live inventory, parts, service, and order data at the pace of a phone call.
Reply, or finish the job
The difference shows up after the message is sent. A chatbot gives an answer. An agent checks the live context, changes the right record, and hands exceptions to a person
Chatbot
AI agent
Follows a script and breaks when the question leaves the path.
Checks knowledge, request history, roles, and connected system data.
Usually answers or collects a form.
Prepares a CRM record, asks for missing data, creates a ticket, or starts the next step.
Mistakes often surface only after a user complains.
Logs answers, sources, escalations, and disputed cases for review.
The agent works where your team already does
Messages come from familiar channels. Knowledge comes from approved documents and databases. The result returns to CRM, 1C, or an internal system, with the source and action logged
Where the request arrives
ChannelsWA
WhatsAppTG 
TelegramWhat the agent runs on
ModelsAI
OpenAIA 
AnthropicAPI and searchAPI custom APIVDB vector databasesWhat it reads and writes back
CRMB24
Bitrix24amo 
amoCRMCRM CRMDocsGS
Google SheetsN 
NotionAT 
AirtableData1C
1CPG 
PostgreSQLSB 
Supabase
Autonomous, with clear boundaries
Before launch, we define what the agent may do, what it must confirm, and which cases always go to a person
- 01
Role-based access
Sources and actions are split by team, branch, or user role. The agent only sees what it needs for the task.
- 02
Source boundaries
Answers come from approved documents, tables, APIs, and knowledge indexes. The agent does not improvise from the open web.
- 03
Audit trail
Every action, source, and handoff is logged, so the team can review a disputed answer step by step.
- 04
Human review
High-risk decisions do not go through unattended. Every escalation has a clear reason and owner.
- 14:02 inbound WhatsApp · request #4187 · returning customer
- 14:02 source Price list, revised 12 Jul · warehouse stock
- 14:02 reply Four models sent, delivery quoted for Astana
- 14:03 write CRM: status → Order #4187, courier delivery
- 14:05 stop Discount is outside the rules → sales manager
Questions before an AI agent project
Short answers about scope, integrations, timing, cost, and production control
A chatbot usually follows a conversation script. An AI agent checks company knowledge and live data, then updates CRM, creates a ticket, or sends a notification. When the decision is risky or data is missing, it hands the conversation to a person with the context attached.
Start with a repeated workflow that has a clear outcome and usable data. Common examples include lead qualification, first-line support, HR screening, and internal document search. Important business decisions should still remain with people.
Yes. Agents usually work on top of existing tools such as WhatsApp, Telegram, amoCRM, Bitrix24, 1C, Google Sheets, Notion, and internal APIs. We check the access model and limits of each integration before development.
We separate access by role, limit approved sources, and log agent actions. Financial, legal, HR, and other sensitive decisions require a human handoff. Disputed answers are reviewed against real conversations and added to the test set.
A narrow working prototype can usually be tested in 1–2 weeks. An MVP with integrations, access roles, logs, and a team interface often takes 3–6 weeks. The exact timeline depends on data quality, API readiness, and the number of workflows.
Cost depends on the number of workflows and integrations, data quality, access requirements, and testing scope. The calculator on this page gives an initial range. After a short workflow review, we define the scope and price of the first stage.
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Let’s discuss the task
Send the workflow, the data sources, and the systems you need connected. We will estimate a practical first stage without hand-waving.