DORA & Accelerate
The workshops draw on a decade of Google Cloud (DORA) research, which proves that high-performing IT teams are achieved through small batches and fast feedback.
We embed AI into the engineering processes of your business
We cut Time-to-Use — the time from the start of requirements discussion to the moment the functionality is put to use. We draw on a decade of DORA, Google SRE and QSM research.
10+years of DORA & SRE research
TTUthe key metric is Time to Use
7–9people — the optimal team size
DORATTUAgentsPlan
Foundation
A scientific approach and global standards
Time-to-Use as a metric
We don't teach coding for its own sake. We teach how to cut TTU — the time from idea to the moment a real user starts getting value from a feature in production.
Loose Coupling
We use AI to ensure architectural cleanliness. Loose coupling is the only statistically proven property of effective architectures.
Catalog
Workshops
Accelerating development of large projects with AI
How to stop drowning in legacy and start delivering value faster.
Accelerating the entire development cycle (Time-To-Use) with AI
From "AI as a chatbot" to "AI as an engineering accelerator" — integrating AI into the TTU methodology.
A personal AI assistant with OpenClaw
From zero to a digital worker in 2 hours — self-hosted, multichannel, zero routine.
AI SecOps: autonomous cybersecurity agents
How to speed up security checks 10x without losing quality — from manual audits to 24/7 agents.
AI technical support agent
A mix of OpenClaw and an AI IDE (Claude Code) that handles basic support tasks: from answering users to automatically configuring the product.
Advanced Claude Code setup
Advanced Claude Code tools and approaches — highly effective AI-agentic development.
AI-native documents and reporting for developers
LLM Wiki + SurfSense: a corporate knowledge base that assembles and updates itself automatically.
AI psychologist: insight into yourself, your team, and mentoring
We'll build a personal agent on Claude Code or Codex that helps analyze communications, chats and voice notes.
Methodology
Plan-driven AI
We teach a system of autonomous agents with a strict structure. This removes the fear that the AI will "hallucinate" something extra or lose context.
What matters is the plan, not the chat
Adding an AGENTS.md file at the project root sets the goals, focus and quality criteria the agent never steps beyond.
Result verification
We teach developers not to take the AI at its word, but to use the chain: "agent → questions → plan → confirmation → execution".
Paradigm
From "coder" to "architect"
Primary Implementer
We draw on the experience of industry leaders (Andrej Karpathy, Simon Willison), who captured the shift: the engineer no longer writes code by hand — they become an architect, specifier and reviewer.
Super Developer 2026
By the end of 2026, manual coding skills will become secondary. We teach how to manage "intent" (Clarity of Intent) — the key engineering competency of the coming years.
Value
Added value for the business
↓ Costs · Savings at scale
Adopting AI lets small teams (7–9 people) maintain the effectiveness that normally drops exponentially as project complexity grows.
↓ Risks · Risk reduction
Using AI to automatically generate and validate documentation in .md + .json formats makes a project transparent and easy to hand over between teams.
Team
Experts
Sergey Korshunov
Overcoming team resistance, a fast move to full-stack, and independent quality assessment of large projects based on Amazon and Google practices.
Alexander Stanovoy
Using AI (Codex and Opus), he onboarded onto a project that had grown for 5+ years without documentation and brought up the dev environment in hours instead of weeks.
Andrey Putin
Embedding AI into the engineering and product processes of enterprise teams, the Time-to-Use methodology.
Alexey Shvets
Automation of engineering processes and agentic development on real enterprise tasks.
Dmitry Krymsky
AI-driven architecture, static analysis of legacy and objective quality control.
Schedule
Upcoming workshops
June 10Accelerating development of large projectsWednesday, 12:30 MSKRUB 20,000 / person
June 17Accelerating development of large projectsWednesday, 12:30 MSKRUB 20,000 / person
June 24Accelerating development of large projectsWednesday, 12:30 MSKRUB 20,000 / person
June 12AI support agentFriday, 14:00 MSKRUB 20,000 / person
June 19AI support agentFriday, 14:00 MSKRUB 20,000 / person
June 26AI support agentFriday, 14:00 MSKRUB 20,000 / person
About Us
We don't teach AI. We accelerate business through AI.
For over 13 years we have delivered large IT projects for the enterprise segment every day. We use our own workshops to train internal teams — which is why we are confident in their effectiveness.
Focus
Changing team behavior, not just knowledge about AI.
Method
Real tasks → adoption → measurable result (TTU).
Foundation
DORA, McKinsey, GitHub Copilot Research — data, not marketing.
90% of AI trainings don't change the business. Because they don't change the way work is done.
01 · Through real tasks
We work with the team's specific business tasks, not abstract examples.
02 · Minimum theory
We build AI into processes with an immediate, measurable result.
03 · System change
We rebuild roles, responsibilities and decision-making within the team.
RoleBeforeAfter
DeveloperWrites code by handDirects the AI agent like an architect
ManagerCollects statuses manuallyGets them automatically
TeamSlow to deliver valueShips and gets feedback faster
DORA / Google
High team performance is tied to the speed of feedback.
McKinsey 2023–2025
AI can boost knowledge workers' productivity by 20–40%.
GitHub Copilot
Developers complete tasks about 55% faster.
A fit
- Companies with an IT product or in-house development
- Teams of 5+ people
- There is a problem with delivery speed
- Readiness to change processes
Not a fit
- Want only training without adoption
- No real tasks
- No readiness to change processes
01 · Diagnostics
We study how the team works and identify where to apply AI.
02 · Workshop
Hands-on work with the team's real tasks.
03 · Adoption
Optional support and scaling of the result.
Contacts
Let's Discuss Your Project
Leave your current contact details and describe your task. We will come back with clarifying questions and a proposal for the next step.
