Fractional CTO · AI Startups
Fractional CTO for AI startups
Build AI products that survive contact with real users — not just demos that wow investors.
The case
AI Startups engineering is not generic engineering.
Every AI prototype works in the demo. Almost none survive the first 1000 real prompts: latency spikes, cost explosions, hallucinations leaking into the UI, evals nobody set up, and a model choice that locked you into one vendor. I help AI founders make the architecture, evaluation, and cost decisions that turn demos into products — working across Claude, GPT, RAG, agents, and custom models.
AI engineering moves too fast for most full-time CTOs to keep current. A fractional CTO who is shipping AI features every week brings recent, opinionated experience without you paying a six-figure salary for someone to learn on your dime.
The first 90 days
What a ai startups engagement actually looks like
A fractional CTO for AI startups brings current, opinionated experience on model selection, evals, and cost control — the three things that turn a demo into a product.
- 01
Weeks 1–2: Eval harness first
Before changing any model or prompt, build the evaluation set that tells you whether a change helped. Teams that skip this are tuning blind and cannot detect the regressions they ship.
- 02
Weeks 3–4: Model benchmarking and routing
Benchmark candidate models against your real prompts, not published leaderboards. Usually the answer is a routing strategy — a small model for easy cases, a large one for hard cases — rather than a single vendor commitment.
- 03
Weeks 5–8: Retrieval and guardrails
Fix the RAG pipeline so it retrieves relevant content rather than vaguely related content, and add structured-output validation so malformed model responses fail loudly instead of leaking into the UI.
- 04
Weeks 9–12: Cost observability
Stand up a dashboard surfacing the top cost drivers per feature and per customer, with caching and prompt compression where they pay. AI invoices get out of hand precisely when nobody is watching.
What we cover
AI Startups-specific decisions I help you make
Tools I use in ai startups
Request a triage
Talk through your ai startups problem.
Free, 30 minutes. Tell me where you're stuck — I'll tell you what it takes. I confirm every request within 24 hours.
FAQ
AI Startups questions founders ask
Should we use Claude, GPT, or an open model? +
Depends on the task and your unit economics. We benchmark candidates against your real prompts, measure quality with evals, and pick the cheapest model that hits your quality bar. Often it is a routing strategy — small model for easy tasks, large model for hard ones.
Do we need to fine-tune? +
Usually no. A strong base model plus retrieval plus careful prompting beats fine-tuning in 80% of cases. We fine-tune when the data and the use case actually demand it — not because it sounds impressive on a pitch deck.
How do you control AI costs? +
Model routing, caching, batching, prompt compression, and a dashboard surfacing the top cost drivers. AI invoices get out of hand when nobody is watching — we set up the watching.