GPT Integration · Kazakhstan
We connect GPT and other LLMs to the places where work already happens: CRM, WhatsApp, Telegram, email, tickets, documents, and internal panels. Model choice comes after the workflow review. We look at the data, error risk, response speed, request cost, and who checks the output.
— 01 / TASKS
This is for buyers who already see why ChatGPT is useful but do not want another separate chat window. They need the model to read the right context, write to the right field, respect access roles, and leave an audit trail.
We add dialog summaries, lead classification, next-step suggestions, reply drafts, or manager hints directly inside the customer record.
Sales and support teams copy less text between tools, while leads can see where AI helped and where a person changed the answer.
We connect the model to existing channels with templates, limits, escalation rules, and safeguards against making promises it should not make.
Replies get faster, while sensitive or unusual messages still go to a person.
AI extracts topic, urgency, customer details, missing fields, and a route: who should handle it, what to ask, and which status to set.
Inbound work stops sitting in one pile and turns into clearer tasks sooner.
We connect the model to policies, contracts, knowledge bases, or catalogs so answers are grounded in your material.
The team gets a sourced draft instead of confident text from model memory.
We compare OpenAI, Anthropic, and other options by quality, latency, price, language support, limits, and data requirements.
The project does not overpay for a heavy model when a simpler setup is enough.
We review current flows, logs, prompts, and integrations, then improve weak spots without a full rebuild when the architecture allows it.
A live tool can be rescued, and the team learns whether the real issue is scenario design, data, or the model.
— 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 assistant for automotive operations: cars, service, orders, part compatibility, and internal knowledge across several data sources.
An AI HR agent for a Kazakhstani retail chain: candidate screening, internal knowledge, vacancy fit, and recruiter workflows.
— 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
Yes. We often compare OpenAI, Anthropic, and other LLMs for a specific workflow: Russian quality, context length, price, latency, data rules, and performance on your own examples.
We take 30-100 real requests, define what a good answer means, and test several options. After that, model choice becomes practical instead of theoretical.
Yes, when there is an API, file exchange, intermediate database, or another reliable integration path. Early on we check access rights and which actions AI may only suggest versus execute.
Not every field has to be sent to a model. We can mask data, restrict sources, keep parts of the logic inside your infrastructure, and log access to sensitive information.
Yes, if we can access the code, flows, logs, and integrations. Sometimes prompts and routing are enough. Sometimes the honest move is to rebuild one broken workflow.
— 10 / LINKS
Send the workflow, the data sources, and the systems you need connected. We will estimate a practical first stage without hand-waving.