01 · Static Analysis
AI Archaeology and Inventory
AI as an "architecture scanner": finding HTTP contracts, queues and hidden dependencies across dozens of repositories — including what was forgotten during onboarding.
Enterprise · Legacy · DORA
Accelerating development of large projects with AI
How to stop drowning in legacy and start delivering value faster.
3 modulesOnline / OfflineEnterpriseCTO / CDTO / Tech Leads
Our clients
Clients and partners
The Problem
The Enterprise "Complexity Trap"
- Slow Onboarding
- Fear of Change
- Loss of Control
- Code for Code's Sake
Approach
An AI-driven approach from KT.Team
AI does not think for you, but it turns the chaos of legacy code into a manageable engineering task.
Program
Workshop Program
02 · Skills & Agents
Knowledge Management and Verification
Plan-driven approach: AGENTS.md, a skills library and the chain "agent → questions → plan → confirmation → execution" instead of blindly trusting AI.
03 · DORA Metrics
Objective Quality Control
We measure impact with DORA: small batches, fast feedback and loose coupling as a proven property of effective architectures.
Schedule
Upcoming Dates
June 10Wednesday, 12:30 MSKRUB 20,000 / person
June 17Wednesday, 12:30 MSKRUB 20,000 / person
June 24Wednesday, 12:30 MSKRUB 20,000 / person
More
Other Workshops
Mission 01
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.
Mission 02
A personal AI assistant with OpenClaw
From zero to a digital worker in 2 hours — self-hosted, multichannel, zero routine.
Mission 03
Hermes Agent: a personal AI assistant for executives
An assistant that lives in familiar channels, remembers context, runs routine tasks, and works with company tools under IT control.
Discuss the AI workshop
Let's learn
Choose a workshop and come with a real process
First we solve the practical task, then we show how it was assembled so the team can repeat the approach safely and independently.
Related Cases
OSNO-VA: AI accountant
- Built an AI platform
AI ingredient recognition by barcode
- Processing sped up from 30 minutes to 2 per batch of 10 images
- Recognition accuracy for composition is 80-95%
OSNO-VA: Financial Agent on MCP
Learn moreRelated solutions
Articles on the topic
- Why enterprise AI agents stall: production workflow vs demo
- Developer and PM estimate: L10-L60, workflow and AI
- Claude in Mid-Market and Enterprise
- What McKinsey, Gartner, BCG, and Sequoia say about AI adoption - and what it means for you
- How to build an AI assistant for business: development, integration, MVP, and scaling to an enterprise system
