AI Transformation for Your Business
I migrate your business processes to AI in practice, not in theory. Deep expertise in agentic development with Claude Code and Codex, in OpenAI and Anthropic models running in production, and in the MCP servers and AI-first software architecture around them.
Oleg Sotnikov runs AI transformation for established businesses hands-on: choosing the processes worth automating, rebuilding the engineering workflow around agentic coding tools and MCP integrations, and staying through the rollout. He did it on his own company first, taking AppMaster from a 25-person team to 2 while the platform held 99.99% uptime for users in 190+ countries and now processes 11B+ tokens a month. Work usually starts with a fixed-price Team & AI Audit ($5,000, five business days) and continues as a fractional CTO retainer.
Transform how your business operates
AI Strategy & Process Migration
I run AI adoption as an operating change: which processes move, in what order, and what each one gives back. I did it on my own company first (AppMaster went from 25 people to 2, with 99.99% uptime held through the transition), so the roadmap comes from having lived it rather than from a deck.
- AI readiness assessment across your workflows, data, and existing systems
- Process-by-process migration roadmap with ROI projections
- Team restructuring around AI-augmented operations
- Change management and hands-on training for the people who use the tools daily
- Measuring what AI returns: cost per task, quality, and throughput
- Clear rules for AI at work: data handling, review gates, and accountability
The environment I build in every day
Anthropic Claude Code Expertise
Claude Code is where most of my engineering happens, across the CLI, the IDE extension, and cloud sessions, with subagents working against real repositories. It's the most capable agentic coding setup I've worked in, and I set it up for teams the way I run it myself: skills, hooks, MCP servers, and CLAUDE.md conventions that hold up in a large codebase.
- Claude Code rollout across CLI, IDE, and cloud sessions, plus team onboarding
- Custom skills, hooks, and slash commands built around your workflows
- MCP (Model Context Protocol) servers connecting your internal tools and data
- Multi-agent pipelines where subagents plan, implement, and review in parallel
- IDE and CI integration for VS Code and JetBrains, with agent runs triggered from pipelines
- CLAUDE.md conventions and context strategy that keep agents on track in big repos
Production-grade AI pipelines
OpenAI & Codex Integration
I build the model layer a business can actually run on: retrieval over your own data, evaluations that catch regressions before your users do, routing between current-generation OpenAI and Anthropic models, and cost controls that keep the monthly bill predictable. A demo takes an afternoon; production is the part I get hired for.
- OpenAI API integration and production pipeline architecture
- Codex for automated code generation, review, and long-running tasks
- Internal assistants and copilots wired into your business workflows
- Token and cost management through prompt caching, batching, and model routing
- Retrieval-Augmented Generation (RAG) over your documents and internal systems
- Model selection and evaluation suites, so an upgrade never changes behavior silently
The engineering system around the agents
AI Software Development Architecture
Agents write a lot of code quickly, which only helps when the workflow around them holds. I design that workflow: sandboxes and permissions, review gates, tests agents can't route around, and observability on what changed and why. That's what lets a small team deliver enterprise-level output.
- AI-augmented CI/CD with automated review on every merge request
- Test strategy for AI-written code, covering generation, coverage gates, and flake control
- Documentation and changelogs generated from the work itself
- Code quality enforced by linting, refactoring passes, and architecture rules agents follow
- Multi-model orchestration that matches model and context budget to each task
- Security, permissions, and audit trails for AI-assisted development
From 25 people to 2, with better results
AI in Action: The AppMaster Story
Staff Reduction
People Doing Work of 25
Tokens Processed Monthly
Platform Uptime Maintained
In 2025, I transitioned AppMaster.io from a 25-person team to a 2-person AI-augmented operation using Claude Code, OpenAI APIs, and custom MCP integrations. I rebuilt every function (engineering, QA, support, content) around AI workflows. The result: better quality, faster delivery, and an 80% smaller team.
Frequently Asked Questions
How can a fractional CTO help an established business?
By restructuring the engineering team around AI so fewer people ship faster, plus modernizing architecture and cutting infrastructure costs. Oleg has 25+ years across both enterprise and startups.
How much can you cut our cloud and infrastructure bill?
Up to 80%, through infrastructure audits: right-sizing resources, removing waste, and re-architecting where it pays off. These are real savings from someone who runs a production startup on roughly $650/month on AWS.
Can you help us transition to AI-first operations?
Yes, practical AI integration that replaces headcount with intelligence. Oleg transitioned AppMaster from a 25-person team to a 2-person AI-augmented operation, an 80% staff reduction, and applies the same playbook to other businesses.
What does an engagement look like for an established company?
It usually starts with a technical and cost audit, followed by a prioritized plan and hands-on or advisory execution across architecture, team structure, and AI adoption.
Ready to Transform Your Business with AI?
Book a free 30-minute call. Let's work out which of your processes AI can improve or automate, and what ROI you can realistically expect.
For the strategy and roadmap side of the work, see AI consulting services