About me
As CTO of a startup in a regulated industry, I built a product from zero with generative AI at its core and scaled it to thousands of users. The hard part was never the model. It was everything around it: the integrations, day-to-day operations, and an architecture that stayed stable at scale.
Today I work as a Fractional Head of AI and AI Solutions Architect: I run scoped POC-to-Production Sprints for teams whose GenAI project is stuck. I come in, find why it isn't shipping (it's rarely the model), and get it there, with spec-driven development, multi-agent systems, RAG, evals and guardrails. Built for production, not demos. Multi-stack and vendor-agnostic: Azure, Anthropic, LangGraph, Semantic Kernel, OpenAI, plus AWS and GCP.
I work both sides: the commercial (tech consulting, team management, client-facing) and the technical (engineering, architecture, hands-on in the code). Full-stack engineer, technical lead, then CTO, with experience across different sectors. If you have a GenAI project that needs to actually ship, let's talk.
Not sure yet where AI creates value in your business? Start with the AI Discovery Sprint.
Track record
Ex-CTO AI in regulated industry
Built a generative-AI product from zero and scaled it to thousands of users. Stable at scale.
GenAI in production · Agents
AI agents, RAG, evals, guardrails. Multi-agent systems built to ship, not to demo.
Spec-driven development
Clear specs first, then build. AI does the mechanical work; humans make the high-order calls.
POC-to-Production Sprints
I find why your GenAI pilot isn't shipping (it's rarely the model) and get it there, scoped.
Multi-stack · vendor-agnostic
Azure, Anthropic, LangGraph, Semantic Kernel, OpenAI, AWS, GCP. I pick the stack that fits the problem.
How I work
The pieces I use to get GenAI into production, and how they connect.
explore how my capabilities connect · hover to discover
View skills as text
Areas of expertise
Szemat.Pro
Technical
- GenAI
- LLMs
- Cloud
- Guardrails
Domain
- Product
- Production
- Architecture
- MLOps
Leadership & people
- Leadership
- Spec-driven
- Systems Thinking
- Teams
- Data Storytelling
- Stakeholders
- Cultural Intel.
Outcome
- Solutions
GenAI in production · Agents
AI agents, RAG, evals, guardrails. Built to ship, not to demo.
AI strategy
What to build, what not to, and how: business vision connected to technical execution.
Fractional Head of AI
I run the AI function on a fractional basis: roadmap, team, high-order decisions.
Spec-driven development
Clear specs first, then build. AI does the mechanical work; humans decide.
Multi-stack · cloud
Azure, AWS, GCP, and Anthropic, LangGraph, Semantic Kernel, OpenAI. Vendor-agnostic.
Regulated industries
AI where stakes and sensitive data matter: privacy, guardrails and traceability.
The outcome
Product + GenAI
Products that ship
Fractional Head of AI + Spec-driven
Teams that ship AI
Multi-stack + Multi-cloud
No lock-in, production-ready
Contact
Have a GenAI project that needs to actually ship? Let's talk.
Track record and domains
Ex-CTO · Fractional Head of AI
- GenAI in production · Agents
- Spec-driven development
- POC-to-Production
- Multi-stack · Cloud