AI development for how business works in Kazakhstan
We design and ship AI agents, RAG systems, GPT integrations, and internal tools for companies in Kazakhstan. The work has to fit local reality: Kazakh, Russian, and English in the same workflow, mixed-language chats, shala Kazakh patterns, WhatsApp, Telegram, CRM, and human escalation when the answer matters.
— 01 / TASKS
Where AI helps in Kazakhstan operations
Useful AI starts with the workflow. We map where the customer writes, who answers, where the data lives, and when the conversation should move to a person.
Support in WhatsApp and Telegram
An agent handles questions in Kazakh, Russian, English, and mixed messages.
Common replies go out faster, while sensitive cases reach an operator with context.
Knowledge search across CRM and files
A RAG system searches policies, PDFs, sheets, amoCRM, Bitrix24, or internal systems.
Staff get an answer with a source instead of piecing it together from five tabs.
Lead and request parsing
AI extracts name, city, language, product, urgency, and next steps from real conversations.
Managers get a usable CRM card even when the customer switches languages mid-chat.
Human escalation rules
We define when AI must stop: complaints, payments, legal risk, VIP customers, or low-confidence answers.
The team keeps control of conversations where mistakes are expensive.
Operations panels
Dashboards for statuses, errors, answer quality, manual reviews, and disputed chats.
Managers can see what happens after launch, not only the demo version.
Documents and internal workflows
Data extraction, version comparison, draft replies, contract checks, and search across instructions.
Legal, HR, and operations teams spend less time copying text by hand.
— 02 / FIT
When custom AI development makes sense
A ready-made tool is fine for personal work. In Kazakhstan, business use gets specific fast: several languages, local CRM habits, messengers instead of ticketing systems, access rules, and staff who need to understand why AI answered the way it did.
Customers and staff write in Kazakh, Russian, English, or switch languages inside one chat.
Data is spread across CRM, WhatsApp, Telegram, Google Sheets, 1C, PDFs, and internal admin tools.
Access rules, action logs, quality checks, and human escalation are part of the product.
After the prototype, you need production work: monitoring, knowledge updates, team training, and support.
— 03 / PROCESS
What the build includes
Task and data audit
We inspect real tickets, documents, spreadsheets, and access rules.
Scenario design
We define where AI replies, where it acts, and where a human stays in the loop.
Prototype
We build a working first version against samples from your actual workflow.
Integrations
We connect CRM, messengers, databases, documents, or internal APIs.
Testing
We test on real dialogs, questions, and files, not just friendly demo prompts.
Launch
We put the system into work with clear roles, logs, and control points.
Quality monitoring
We review wrong answers, edge cases, escalations, and user behavior.
Support and iteration
We improve scenarios after launch, once real usage starts showing the truth.
— 04 / WORK
Relevant work
This is not a logo wall. These projects show similar systems: agents, RAG, support workflows, internal tools, and integrations.
AI Agent · HR · Enterprise
Magnum HR Agent
An AI HR agent for a Kazakhstani retail chain: candidate screening, internal knowledge, vacancy fit, and recruiter workflows.
RAG · Retrieval · US MarketAutomotive RAG Assistant
An AI assistant for automotive operations: cars, service, orders, part compatibility, and internal knowledge across several data sources.
AI Agent · Events · Participant SupportAlmaty Marathon
An AI support agent for one of the largest running events in Central Asia, answering participant questions and routing edge cases.
— 05 / INTEGRATIONS
Integrations
In Kazakhstan, AI projects often depend on the CRM, messengers, and messy file storage. We check the source of truth, available APIs, data ownership, and how updates will reach the system.
CRMWhatsAppTelegramGoogle SheetsNotionAirtable1CBitrix24amoCRMPostgreSQLSupabaseOpenAIAnthropiccustom APIvector databases
— 07 / TIMELINE
Timeline and working format
Fast audit
2-3 business days when sample data and a process owner are available.
Prototype
1-2 weeks for a narrow scenario with a limited integration set.
MVP
3-6 weeks when the system needs real integrations and team access.
Production
Timeline depends on integrations, data quality, and security requirements.
— 08 / PRICING
Pricing
Pricing depends on integrations, data quality, access roles, testing scope, and infrastructure requirements. Each stage is paid separately.
Discovery
A paid review of the task, data, risks, and first sensible scope.
Prototype
We test the scenario on a small data set before debating it in theory.
MVP
We build a working version with UI, integrations, and basic quality control.
Production system
We harden the system for access control, logs, operations, and support.
Support
We monitor quality, fix issues, and add new scenarios after launch.
— 09 / azamat.ai
Why azamat.ai
We design AI around process, data, and responsibility, not around a polished prompt demo.
We can handle multilingual scenarios: Kazakh, Russian, English, transliteration, and mixed chats.
We understand the local operating stack: CRM, WhatsApp, Telegram, spreadsheets, manual approvals, and escalations.
The founder stays involved in architecture and key decisions, especially early on.
Logic Layer LLP stands behind the brand, with real work in HR, RAG, events, and internal products.
— 10 / FAQ
FAQ
Yes. We start with real conversations and look at how people actually write: formal Kazakh, Russian, shala Kazakh, transliteration, typos, and short voice-message transcripts. Then we design the answers and tests.
Yes, if there is a reliable integration path: API, webhooks, export, or middleware. Early on, we check limits, access rights, message templates, and the source of truth.
A narrow workflow usually takes 3-6 weeks. If the first release needs several languages, CRM, roles, security, and a review panel, we scope production separately.
The most useful inputs are 30-100 real chats, sample documents, a list of systems, staff roles, and escalation rules. The knowledge base does not need to be perfect.
It depends on integrations, data quality, languages, security requirements, and testing depth. We usually start with discovery so the first stage can be estimated honestly.
Tell us what you're building.
Start with a few detailsWe 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.