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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. We help AI founders make the architecture, evaluation, and cost decisions that turn demos into products — with deep experience 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.

What we cover

AI Startups-specific decisions we help you make

01 Model selection and routing (Claude vs GPT vs open) for cost and quality
02 Evals that actually catch regressions
03 RAG architectures that retrieve relevant content, not "vaguely related" content
04 Hallucination guardrails and structured-output validation
05 AI cost dashboards before the first surprise invoice

Tools we use in ai startups

Claude APIOpenAIpgvectorPineconeLangChainLlamaIndexHeliconeModalReplicate

Book a call

Talk through your ai startups problem.

Free 30-minute technical review. Tell us where you're stuck — we'll tell you what it takes.

Free 30-min technical review

Tell us where you're stuck. We'll tell you what it takes — honestly.

Open booking page

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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.