What a fractional CTO for AI startups actually does
In the first two weeks I audit what exists — codebase, data, infrastructure, costs, team — and write down the target architecture and the ordered list of things to fix or build. From there the role is steady: architecture and technical roadmap ownership, daily code and PR reviews, sprint planning with your product lead, engineer mentoring and pairing, take-home design and interviews when you hire, vendor and model-provider negotiation, and a written record of every significant decision so the next engineer understands why.
AI startups have a few decisions that are specific to them and I spend a lot of my time on those: model selection and routing across OpenAI, Anthropic Claude, Groq and open models; whether retrieval or fine-tuning is right; how to evaluate quality so releases do not silently regress; how to keep API costs from eating gross margin; and what data you must collect now to have any moat in a year.
- Target architecture, technical roadmap and build-versus-buy decisions
- Model, provider and vendor selection with cost and risk analysis
- Evaluation strategy so AI quality is measured and regressions are caught
- Daily PR review, pairing and mentoring for your engineers
- Hiring: role definitions, take-home tests, technical interviews
- Investor and customer due diligence support on the technical side