Strategy, architecture, and hands-on implementation of LLM-backed systems — without the hype. We work with engineering teams who need AI features that survive contact with real users.
Roadmaps that connect business outcomes to model capabilities, data realities, and shipping constraints. Build vs. buy decisions you can defend.
End-to-end build of agents, RAG pipelines, and LLM-backed product features. From prototype to production with evals, observability, and graceful failure modes.
Audits of existing AI systems for cost, latency, reliability, and quality. Concrete remediation plans, prioritized by risk and ROI.
Workshops, pairing sessions, and review cadences that upskill your engineers on modern AI tooling — so the capability stays in-house.
Fixed scopes, fixed prices, no hourly meters. Custom engagements are available when none of these fit.
A focused diagnostic for teams that have something running and aren't sure if it's working.
Best for teams with an AI feature already shipped or in pilot.
Take a well-defined problem from idea to a working, evaluable prototype in your stack.
Best for teams committing to ship an AI feature this quarter.
Embedded technical leadership for AI initiatives — strategy, hiring, code review, hands-on when needed.
Best for early-stage teams without senior AI leadership in-house.
Goals, constraints, data, and existing stack. What success looks like, and how we will measure it.
A minimal, evaluable architecture. Clear interfaces, explicit tradeoffs, and a path from prototype to production.
Ship, instrument, document, hand off. No black boxes — your team owns the system afterward.
Yarmolenko Consulting is the independent practice of Vladimir Yarmolenko, a software engineer with 26 years of experience building and leading engineering teams.
Vladimir spent more than two decades at Microsoft — first as a software engineer, then for over fifteen years leading engineering teams shipping production software at global scale. He went on to serve as CTO at Oberon Security, a cybersecurity startup, where he built and led the engineering organization end-to-end.
At Oberon, Vladimir designed and built an agentic AI system for security incident triage. Validated against real Microsoft Sentinel data, the system reached roughly 90% agreement with analyst-labeled ground truth — a working prototype aimed at reducing alert fatigue in production SOC environments. Current engagements continue this thread: applied AI on real data, with measurable evaluation, in stakes-sensitive domains.
That trajectory — large-scale systems at Microsoft, end-to-end startup leadership, and hands-on agent engineering on production data — shapes how engagements run today: ship the smallest thing that produces measurable value, build evals to know it's working, and leave the team a system they can extend on their own.
Send a short note about what you're building and where you're stuck. You'll hear back within two business days.