AI becomes valuable only when it survives contact with real systems: data, legacy platforms, delivery constraints, governance, users, and business metrics.
Enterprise AI in Production
My work is focused on that transition ā from AI ideas and prototypes to production-ready enterprise products.
Over the past 18 years, I have worked across enterprise IT, CRM, CDP, data platforms, analytics, and AI/ML solutions. For the last several years, my focus has been on GenAI, RAG systems, AI-powered analytics, LLM-driven automation, and the practical architecture needed to make these systems usable in real business environments.
The core of my experience is AI productization: shaping feasible solutions, translating business needs into architecture and requirements, aligning stakeholders, and guiding cross-functional teams from discovery to delivery. This includes technical discovery, prototyping, architecture review, presales, delivery planning, and implementation oversight across engineering, ML, DevOps, analytics, and client-facing teams.
I have delivered technology initiatives for enterprise clients across FMCG, retail, automotive, telecom, and marketing technology, including Philip Morris, PepsiCo, VW, BMW, and Mazda.
Current Focus
A major part of my current focus is enterprise AI adoption beyond the demo: selecting realistic use cases, designing operating models, controlling context, evaluating outputs, managing cost, reducing risk, and connecting AI capabilities to measurable outcomes.
Iām also working on Onion, an organizational control plane for AI-assisted software engineering. The core idea is simple: coding agents should not operate as individually configured, unmanaged tools. They should inherit the role, permissions, policies, tools, and working environment of the people they assist. Onion helps engineering organizations govern how humans and their AI agents work together by centrally defining capabilities, delivering the right operational assets, and continuously rolling out engineering standards across teams and codebases.
Notes and Case Studies
This site collects my notes and case studies on enterprise AI, GenAI productization, CRM/CDP and data platforms, AI agents, RAG, codebase understanding, and the gap between AI prototypes and production systems.