AI Agents Development · Kazakhstan
We build agentic systems that go beyond a polished demo: the agent understands the task, works with data and tools, hands hard cases to people, and leaves a clear trace in the system.
— 01 / TASKS
An AI agent makes sense when a conversation should become an action: checking a candidate, finding a rule, creating a ticket, updating CRM, preparing a reply, sending a notification, or collecting missing data.
We map the current "lead and request qualification" workflow: who asks, where the data lives, what counts as a good answer, and when a person should step in.
The team spends less time sorting work by hand and gets clearer next steps.
We map the current "hr candidate screening" workflow: who asks, where the data lives, what counts as a good answer, and when a person should step in.
Users get faster answers while hard cases still reach a person.
We map the current "customer and participant support" workflow: who asks, where the data lives, what counts as a good answer, and when a person should step in.
Managers can see statuses, errors, and scenarios that need improvement.
We map the current "document and knowledge-base search" workflow: who asks, where the data lives, what counts as a good answer, and when a person should step in.
Data stays inside the working system instead of spreading through private chats.
We map the current "draft replies and crm actions" workflow: who asks, where the data lives, what counts as a good answer, and when a person should step in.
After the MVP, the scenario can grow without rebuilding the whole system.
We map the current "conversation quality control" workflow: who asks, where the data lives, what counts as a good answer, and when a person should step in.
The company gets quality control, not just another bot.
— 02 / FIT
Custom development is useful when an off-the-shelf tool does not understand your data, access rules, systems, or responsibility boundaries.
— 03 / PROCESS
We inspect real tickets, documents, spreadsheets, and access rules.
We define where AI replies, where it acts, and where a human stays in the loop.
We build a working first version against samples from your actual workflow.
We connect CRM, messengers, databases, documents, or internal APIs.
We test on real dialogs, questions, and files, not just friendly demo prompts.
We put the system into work with clear roles, logs, and control points.
We review wrong answers, edge cases, escalations, and user behavior.
We improve scenarios after launch, once real usage starts showing the truth.
— 04 / WORK
These projects are close in shape: integrations, knowledge, operations, support, or product AI logic.
An AI HR agent for a Kazakhstani retail chain: candidate screening, internal knowledge, vacancy fit, and recruiter workflows.
An AI support agent for one of the largest running events in Central Asia, answering participant questions and routing edge cases.
An AI assistant for automotive operations: cars, service, orders, part compatibility, and internal knowledge across several data sources.
— 05 / INTEGRATIONS
Before the build, we check which systems expose APIs, where data lives, and who will keep it current.
— 06 / DATA
We design the architecture around your requirements: roles, access rules, action logs, source restrictions, and answer checks
— 07 / TIMELINE
2-3 business days when sample data and a process owner are available.
1-2 weeks for a narrow scenario with a limited integration set.
3-6 weeks when the system needs real integrations and team access.
Timeline depends on integrations, data quality, and security requirements.
— 08 / PRICING
Pricing depends on integrations, data quality, access roles, testing scope, and infrastructure requirements. Each stage is paid separately.
A paid review of the task, data, risks, and first sensible scope.
We test the scenario on a small data set before debating it in theory.
We build a working version with UI, integrations, and basic quality control.
We harden the system for access control, logs, operations, and support.
We monitor quality, fix issues, and add new scenarios after launch.
— 09 / azamat.ai
— 09 / FAQ
A chatbot usually guides a user through a script. An AI agent works with context, data, and tools: it can find a rule, prepare a record, request missing data, and hand hard cases to a person.
The best candidates are repeatable workflows with a clear outcome, available data, and escalation rules: requests, HR screening, support, document search, lead qualification, and quality control.
We test the agent on real examples, uncomfortable questions, and failure scenarios. Before launch it needs logs, access roles, a test set, clear escalation, and a process owner on the client side.
We separate access by role, restrict sources, log actions, and keep people in control of important decisions. The agent should not make HR, legal, or financial decisions without review.
Usually we build on top of what already exists: CRM, documents, WhatsApp, Telegram, spreadsheets, and internal APIs. A new interface is added only when the team needs it for control, setup, or operations.
— 10 / LINKS
Send the workflow, the data sources, and the systems you need connected. We will estimate a practical first stage without hand-waving.