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
The first workflow is better narrow. Something that comes in every day, where the data already exists, and where you can tell whether the answer was right
A fixed FAQ rarely needs an agent — a normal bot will do. The agent pays off when the reply depends on data, on who is asking, and on what should happen next
Without CRM fields, status, the right document or history, the reply is basically a guess.
Someone on the other end is waiting for a record, a draft, a notification or a ticket — not just a chat reply.
The team needs logs, sources and a way to fix the agent when it answered wrong.
HR, legal and financial calls don't go through automatically. The agent stops and brings in a person.
A scripted bot is fine while the script is fine. The agent comes in when the answer has to look at data, at who is asking, at the status of the ticket and at what needs to happen in another system
Follows a script and breaks when the question leaves the path.
Usually answers or collects a form.
Mistakes often surface only after a user complains.
Checks knowledge, request history, roles, and connected system data.
Prepares a CRM record, asks for missing data, creates a ticket, or starts the next step.
Logs answers, sources, escalations, and disputed cases for review.
Already have one repeated workflow? We can treat it as the first agent candidate.
Discuss the workflowThese projects share the same foundation: agents, RAG-style retrieval, support workflows and AI on top of operational data
We plug into whatever the team already works in: messengers and CRM, documents and databases, internal services, LLM providers. If a system has an API, we wire in directly. If it doesn't, we build the connector around the actual process.
WhatsApp Channels 
Telegram Channels 
Bitrix24 CRM 
amoCRM CRM 
Google Sheets Docs 
Notion Docs 
Airtable Docs 
1C Data 
PostgreSQL Data 
Supabase Data 
OpenAI Models 
Anthropic Models The agent has to know where it's allowed to act on its own, where it has to ask, and where it stops and calls a person.
Sources and actions are split by team, branch or user role — the agent only sees what it's meant to.
Answers come from approved documents, tables, APIs and knowledge indexes. The agent doesn't wander off to the open web.
Every action, source and handoff is logged. When something goes sideways, you can pull it up and walk through what happened.
High-risk decisions never go through unattended. Escalation has an explicit list of cases and an owner.
Each stage has a concrete output and a place where you can stop. You don't have to sign up for a full production rollout before you can see whether the first workflow actually works.
Paid output: Workflow map, data check, risks, first scope
Client input: Real examples, system access owner, process owner
Decision: Pick the first workflow or stop cleanly
Paid output: Working agent path on a limited data set
Client input: Sample data, test questions, review feedback
Decision: Promote to MVP only if answers and stops are reliable
Paid output: Integrations, interface, logs, team access
Client input: API access, roles, launch owner
Decision: Launch to a controlled group
Paid output: Hardening, monitoring, support, next workflow
Client input: Operational feedback and quality review
Decision: Expand only when the first workflow holds up
We reply within one business day. Then Azamat joins every first call personally, so you get an honest scope, budget, and fit from the person responsible for delivery.