AI literacy for leadership
We explain what models can do, where they fail, how to evaluate AI ideas, and which questions leaders should ask.
Leaders make calmer decisions and avoid buying AI because the demo looked impressive.
Corporate AI training
Corporate AI training
We run practical AI training for leadership, operations, and teams who need to use models at work, not sit through another future-of-AI talk. The session uses company examples: emails, tickets, reports, policies, spreadsheets, CRM scenarios, and internal documents.
AI agents, RAG, and internal tools20+ launched projectsThe team behind azamat.ai and Logic Layer LLP
— 01 / TASKS
What the corporate AI training covers
The program is built around your work. First we learn where the team already uses ChatGPT or other models, where the risks are, and where AI can remove manual effort quickly.
Prompts for real work
Teams practice on real materials: emails, reports, ticket analysis, document drafts, meeting prep, and internal notes.
Employees leave with techniques they can use the same day.
AI in operations
We look at where models can classify, check, summarize, search, and prepare the next step in a workflow.
The team sees practical AI inside its own process, not a generic tool tour.
Internal AI usage rules
We define what data can go into models, what must stay out, when human review is required, and how outputs should be stored.
The company gets a clear safety baseline instead of rumors and blanket bans.
Hands-on company examples
Participants work with anonymized or approved company materials instead of internet exercises.
The session leaves behind templates, scenarios, and pilot ideas.
AI opportunity map
We sort tasks into what the team can handle with skill, what needs a GPT workflow, and what deserves an integration or agent.
The team knows what to do itself and what should become a separate project.
— 02 / FIT
Who this training is for
This format fits companies where AI has already entered daily work but still feels messy: one person writes strong prompts, another pastes sensitive data into a public chat, someone else expects full automation next month.
- Leadership needs to understand where AI is useful and where it creates risk.
- Operations teams want faster work with tickets, reports, documents, and internal knowledge.
- HR, sales, support, or back office teams already use ChatGPT without shared rules.
- The company wants to prepare people before a GPT integration, AI agent, or internal AI pilot.
— 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
Case work that makes the training concrete
We use real types of work to explain the difference between personal AI skill and product-grade systems: HR agents, RAG over documents, communities, events, and internal tools.
Kazatomprom AI Training
A two-day applied AI program for Kazatomprom teams: ChatGPT basics, meeting analysis, business writing, reporting anomalies, counterparty checks, sanctions risks, and transaction analysis.
- Built
- Workshop program, practical prompts, meeting and document workflows, and applied finance, procurement, and risk scenarios.
- Stack
- ChatGPT · Prompt engineering · Meeting intelligence · Risk analysis
KEGOC AI Training
An AI briefing for KEGOC on generative AI, ChatGPT, productivity, infrastructure monitoring, grid optimization, and practical use inside an energy company.
- Built
- Training deck, executive examples, ChatGPT demonstrations, and energy-sector AI scenarios.
- Stack
- Generative AI · ChatGPT · Energy analytics · Infrastructure monitoring
Astana Group AI Training
A 2024 foundations workshop for Astana Group, built at the point when generative AI had moved from headlines into boardroom questions, but most teams still needed a practical way to use it at work.
- Built
- A live AI literacy program: the evolution of generative models, ChatGPT, prompt structure, business writing, meeting summaries, document work, ideation, verification habits, and first use-case mapping.
- Stack
- ChatGPT · Generative AI · Prompt design · AI literacy · Business workflows
Kusto Group AI Training
A 2024 AI foundations session for Kusto Group leaders: a calm, business-first briefing on what generative AI can support, what it cannot own, and how to start without confusing a demo with implementation.
- Built
- Leadership-oriented AI training: ChatGPT fundamentals, prompt logic, model limits, verification habits, business scenarios, adoption choices, and the first decision map for responsible use.
- Stack
- ChatGPT · Generative AI · Prompt design · Leadership briefing · AI adoption
Parasat Business Club AI Talk
A May 22, 2026 talk for Parasat Business Club about AI as a CEO working layer: idea pressure-tests, financial thinking, messy inputs, company knowledge, agents, and sharper tasks for a team.
- Built
- A 60-minute executive talk built from a practical owner workflow: AI as thinking partner, finance analyst, knowledge layer, agent interface, implementation filter, and use-case canvas.
- Stack
- CEO workflow · ChatGPT · Claude · Knowledge bases · AI agents · Use-case canvas
— 05 / INTEGRATIONS
Integrations
The training does not connect every system on day one, but we show where AI can sit next to CRM, WhatsApp, Telegram, Google Sheets, documents, and internal APIs.
CRMWhatsAppTelegramGoogle SheetsNotionAirtable1CBitrix24amoCRMPostgreSQLSupabaseOpenAIAnthropiccustom APIvector databases
— 06 / DATA
Rules, data, and responsibility
We discuss internal AI usage rules directly: which data should not go into public models, how outputs should be checked, and where a human must stay responsible.
- We separate safe personal use, shared team templates, and tasks that need a real integration.
- We name restricted data clearly: personal data, commercial terms, private documents, credentials.
- We show how to check model output against sources, facts, and common sense.
- For leadership, we prepare plain language for explaining AI rules inside the company.
— 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 do not give a lecture about the future of AI. We work through real processes, prompts, data, and limits.
- Our work covers AI agents, RAG, GPT integrations, HR, support, events, and internal tools.
- We can work with leadership, operations, and technical teams in the same engagement.
- Training can run in Russian or English, in person in Almaty or remotely.
- After training, the next step can be a pilot: an agent, GPT integration, RAG system, or internal tool.
— 09 / FAQ
FAQ
How long is corporate AI training?
A focused intro session is usually 2-4 hours. For leadership and operations, separate 2-hour workshops often work better than one large mixed session.
Can we use our own documents and examples?
Yes. That is the best format. We use anonymized or approved materials: tickets, emails, instructions, reports, spreadsheets, and policies.
Is this ChatGPT training or broader AI training?
We start with practical LLM work: ChatGPT, Claude, Gemini, or your corporate tools. Then we show where RAG, integrations, and AI agents become necessary.
Do participants need technical knowledge?
No. Most tracks only require people to understand their own work. Technical depth is added for IT, product, or analytics teams.
Can you help write our internal AI usage rules?
Yes. We can prepare a short working document: what is allowed, what is restricted, which data must stay out, and when human review is required.
— 10 / LINKS
Let’s discuss the task
Tell us which team needs training, what tools employees already use, and which examples we can work through. We will suggest a session format built around your actual work.