For CTOs · CIOs · Heads of Development · Architects

AI, architecture and developer platforms for companies

I help technology companies build AI into the entire development cycle — from requirements and architecture to code, testing and delivery. I start with a diagnostic and finish with a working engineering system.

Discuss your project Start with an AI development audit

Services

Every engagement starts with a diagnostic and ends with a result that stays with you

01 AI Development Audit An audit of how AI works across the entire development cycle — from requirements to delivery — with an adoption plan. How it works → 02 Coding Agents Agents in the engineering process — tasks, context, an isolated environment, checks and review. How it works → 03 Legacy Modernization with AI Modernizing legacy systems — recovering knowledge, tests, step-by-step migration with AI. How it works → 04 Architecture Audit An independent architecture assessment — risks, growth constraints and well-founded decisions. How it works → 05 Platform Engineering An internal developer platform — delivery pipeline, environments, standards and a foundation for AI agents. How it works → 06 Private / Local AI AI inside a closed perimeter — local models, a gateway, data classification and security requirements. How it works →

AI Development Platform

AI as part of the engineering platform, not a pile of subscriptions

A single access point to models, managed corporate context, an isolated environment for agents, policies and metrics — set up once for all teams.

Platform Engineering →

01 · teams & tools

IDECLI agents CI/CDTask tracker

02 · platform services

Agent Platform
sandbox · task → PR Context Platform
code · docs · API · tasks Model Gateway
routing · limits · cost

03 · governance

policies · RBAC/ABAC · audit · secrets · data classification

04 · metrics

lead time · deploy freq

05 · models

Cloud LLMLocal LLM

First step · diagnostic

AI Development Audit

Where AI already works in your development cycle, where it doesn't, and why: a bottleneck map, an assessment across seven dimensions and a plan for 30 and 90 days.

Discuss your project

Questions and answers

The essentials in brief

Where should AI adoption in software development start?

With a map of the delivery flow — where tasks wait the longest — and a metrics baseline. Then AI is connected to the one or two stages with the longest waits. The free AI Engineering Assessment (in Russian) gives a profile of your company across seven dimensions in five minutes.

What is a coding agent and how is it different from an AI assistant?

An assistant answers a question or completes code, and a human performs the steps. An agent receives a task and goes through the cycle on its own — study the code, change it, build, test, fix, open a pull request. That is why an agent needs context, an environment and automated acceptance.

Can AI be used with corporate code?

Yes, once it is defined which data may be sent to which models. Usually that means enterprise plans that prohibit training on your data, a model gateway with audit and secret protection, and local models for the most sensitive data.

Can LLMs run inside the company?

Yes. Open models run on your own servers, and data never leaves the perimeter. On complex tasks they still lag behind the best cloud models, so a hybrid setup with routing by data class is more common.

How much does AI adoption cost?

An honest estimate is only possible after a diagnostic — it depends on team size, the state of your processes and security requirements. Any figure named before getting to know the process would have to be made up.

Next step

Let's discuss your project

Tell me what is happening with your development and what you expect from AI. I reply personally and suggest a format — a diagnostic, an audit or a consultation.

Discuss your project Technology partnerships