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Corporate Training for Working with AI
We run corporate training on AI tools: large language models (LLM), prompt engineering, generative AI, and routine task automation. We help embed AI into company processes: programs for managers and teams, real-case reviews, plus safety and responsibility practices when working with AI.
We teach teams and managers to use AI at work: from prompt engineering to process automation.
Pricing
from ₽600,000
per program
This is the lower bound of a project. We scope the exact quote after a brief: it depends on volume and the number of integrations.
Capabilities
Hands-on AI tools
We teach working with LLMs and generative AI: ChatGPT and similar, document processing, data analysis, and content creation.
Prompt engineering
We master effective prompting: structure, context, and iterations that produce accurate, useful answers.
Embedding AI into processes
We find tasks that can be handed to AI and automate them: from emails and reports to customer inquiry handling.
Safety and responsibility
We teach safe AI usage: data protection, confidentiality, fact-checking, and corporate policies.
From request to result
1
Team diagnosis
We assess current AI skills, tasks, and tools. We set learning goals and target roles.
2
Training program
We build the program: base, advanced, and management tracks. We work through your team's real tasks.
3
Practice on your processes
Employees solve their own working tasks: emails, reports, data analysis, content. We solidify through doing, not slides.
4
Rollout and support
We help launch AI practices at work, prepare internal guidelines, and answer questions after the course.
Check if it fits you
Good fit
A team without a systematic AI skill
Employees try AI chaotically, without rules or process value. Training adds a system and practice.
Embedding AI into processes
The company decided to automate routine, but the team does not know how. The course prepares the ground.
Not a fit
One-off skill needs
A few employees need one AI technique without scale. Short consultations beat a full course.
Strict data caution
Tough confidentiality requirements call for policies before mass training, or the course will clash with practice.
What you get
70%
of attendees applied AI at work within a month
A program for managers and operations teams: base tools, prompt engineering, and their own process reviews. After the course, teams launched first scenarios and shed routine.
Source and figure
Verifying answers
Employees start verifying answers: the source, the figure, the regulatory document. Skepticism about results removes errors that cost more than saved minutes.
A month in
Time on routine
Time on routine texts drops noticeably within a month: a client email, a post draft, a meeting summary. You see the freed hours yourself.
A block list
Data boundaries
The team fixes what should stay out of public models: client personal data, commercial terms, documents under NDA. Boundaries are discussed on real examples.
Tested templates
A prompt library
Templates for typical tasks are gathered in a department library, so a new hire starts with tested formulations. Prompts stop being a private initiative.
Speed and accuracy
Criteria for managers
A manager gets criteria to evaluate AI work: speed, accuracy, number of iterations. Without metrics, replacing manual work looks like a matter of taste.
Sticks for good
Practice on your tasks
A course without review of your tasks is forgotten in two weeks, so the effect sticks through practice on your own processes. We hand over the rollout plan with the program.
How the system is built
A task audit
We start with an audit of the team's tasks: we see where hours are lost to routine and which processes ask for automation.
A program for your processes
We build the program for your processes and roles, so practice runs on real tasks from your day-to-day work.
Practice on your cases
Participants review their own cases with the trainer, so the skill sticks at work. Practice runs on your documents and everyday tasks, so the team keeps the new tools.
Metrics and rollout
A manager gets evaluation criteria: speed, accuracy, and iteration count, and we hand over the rollout plan with the program.
Where the solution pushes back
- 01
A model invents facts
A model confidently invents facts, and people trust the confident style of the answer. We teach checking against a primary source, the most valuable skill of the course for most teams.
- 02
Personal data in a prompt
Personal data in a prompt is more dangerous than it looks: the service processes the request on its own servers. We go through what data each tool may take and hand over a desk cheat sheet.
- 03
The person's role shifts
Bringing AI in moves the person from executor to reviewer: a mistake by the model shows up in the final result in front of the client. The role shifts, and we prepare for that transition.
- 04
One model for every task
Analyzing spreadsheets and generating images need different tools, so one model goes only so far. We compare the tools on your cases and show the difference on facts.
- 05
Automation for its own sake
Automation for its own sake costs more than manual work: a common mistake is building a process for half a percent of output. We count hours saved on real volumes before integration.
- 06
Manager support
Training sticks when the manager expects a new way of working. We build programs for whole teams: without manager support employees slip back to old habits within a month.
Cost calculator
This is an indicative range, not a quote. We scope the exact estimate after a brief: it depends on volume and the number of integrations.
Participants
Program depth
Format
Service
Corporate Training for Working with AI
The estimate covers our team's work. Hardware, third-party service licences and travel are added on request.
Estimated cost
₽1.43M
Timeline
7 wk
Frequently asked questions
Which roles does it fit?
Marketing, sales, support, analytics, and management. We build the program for each role's real tasks instead of teaching everyone the same.
Is it live or online?
Both: in-person workshops, online courses, and hybrids. Practice runs on your data and processes.
Do we need technical knowledge?
No, the base track is for everyday employees. Advanced topics and automation go in a separate technical track.
What about data security?
We cover what can and cannot be loaded into AI, how to verify facts, and what policies to introduce. A separate module for security and managers.
How does the program start?
With an audit of the team's tasks: we see where hours are lost to routine and build the program for your processes. Practice runs on real examples, not abstract exercises.
What teams add to this service
Related Services
Part of Solutions
Implementation Examples
Ready to discuss your task?
Leave a request – we'll get back with a proposal and a preliminary estimate.
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