This website uses cookies to ensure you get the best experience
Leave a request
Fill in your details and we will contact you shortly.
We train teams to use AI tools and build processes for the new AI-driven reality
into an AI-native organization
Turn your company
Our experts serve on program committees for leading AI conferences
EORA has implemented 80+ AI solutions. We help companies turn AI into a practical business tool.
We teach teams how to implement AI and build deeper expertise in the field
EORA has been implementing AI in business processes since 2016
Turn your company into an AI-native organization
Briefing session for the CEO / C-level
The session format is
a strategic session lasting 2−3 hours online or 5−6 hours offline
Deliverables
Guidelines for organization and quality control
Comprehensive list of strategic questions (owner, metrics, steps)
90-day implementation roadmap
The session program includes:
Al economics and scaling solutions
Risk, security and managing AI
AI solution architecture: LLMs, RAG, agents, and data
Where AI can create the greatest business impact
The goal is
to understand where to invest, how to manage risks, and how to launch 1−3 pilots with measurable results.
Top management training
The session format is
2−4 sessions of 2 hours each, or a 1−2 day workshop. Between sessions, participants complete homework based on real tasks.
Deliverables
List of pilot initiatives with metrics and owners.
Internal guide / regulation (draft) + implementation plan.
Department scenario library (ready-made "recipes").
The session program includes:
Design of regulations: roles, rules, control, training newcomers.
Building a "library of scenarios" and internal guidelines.
Quality and validation: verification, sources, control.
Department process map: where AI helps and where risks lie.
The goal is
Create practice leaders, quality standards, and a scenario library so implementation does not depend on a single training session.
Employee training
The session format is
Online master class lasting 1.5−2 hours, or a series of 2−3 sessions. Practice is based on company examples.
Deliverables
Collected initiatives from employees
A guide on safety and ethics.
A set of templates and prompts tailored to specific roles.
A checklist on "how to use AI at work".
The session program includes:
Fact-checking
Quality control of results and errors
5–10 typical scenarios: emails, reports, meeting summaries, analysis, ideas.
How to give tasks to AI
The goal is
Build basic literacy and provide working scenarios for daily tasks so employees can start using AI the next day.
Training programs
Safe AI usage rules
AI results checklist
Ready-to-use templates and prompts
Scenarios of using Al in repetitive tasks
Prioritizing Al initiatives
Al efficiency metrics
Processes and quality control for AI solutions
AI use cases for different business functions
90-day Al implementation plan
Calculation of risks and managing AI
Understanding where AI can create the greatest business impact
Companies trust us to train their teams
Next steps after training
Team training is only the first step
Training is the entry point. After that, we help turn ideas into real projects.
Design pilots and benchmarks
Develop quality metrics and measure business impact
Consulting on LLM and AI implementation
Our pros
All our programs are based on real projects, not theory
years of AI development
Program committee participation at leading AI conferences: OpenTalks. AI and Tech week by South Hub
10
80+
AI/LLM IMPLEMENTATION PROJECTS
Speakers
Sergey Verentsov
Winner of an international computer vision hackathon
Managed AI implementation projects for 12 companies from the RBC Top 100 ranking
Leading expert in AI implementation: scenarios, architecture, data, access, quality, and operations
"Technology and tools change faster than textbooks. The only people who can truly learn in this environment are those who practice constantly."
Alexander Blinov
He leads the implementation of AI products from the formalization of business tasks, to stable launch and support.
His role is to turn complex requirements into understandable architecture and manageable stages of development, ensuring a predictable outcome.
Emil Mageramov
Expert in machine learning at the intersection of IT and biotechnology
Expert in data science
VIOCAD's computational chemistry group leader and Skillfactory lecturer. His expertise lies in the use of AI in biopharma, drug development, and complex interdisciplinary tasks.
The problem is not a lack of knowledge. It is that learning is often disconnected from objectives, metrics, and implementation.
Why does AI have a slow impact in many companies?
Accelerated the unnecessary. We automated non-critical tasks to focus on key issues.
Magical thinking. Without a clear understanding of the process, the tool cannot deliver value.
Rushed conclusions can be misleading. One failed attempt is not enough to judge the potential.
There is no result without tasks. Experiments are often disconnected from processes and KPIs.
