AI startups have a specific product management problem: most PMs can't tell the difference between a demo and a product. A retrieval pipeline that impresses in a pitch falls apart on real customer data; an agent that works in the happy path burns trust the first time it hallucinates a refund. A fractional PM for an AI startup has to know where those failure lines are — from building, not from reading about them.
I co-founded WisOwl AI, an agentic hiring platform for the Indian job market, and built its embedding-based semantic matching engine on FAISS and Supabase pgvector. It grew to 5,000+ organic signups and 15+ recruiter partnerships with zero paid marketing. When I say I'll scope your AI feature, I mean at the level of chunking strategy, retrieval evaluation, latency budgets, and what happens in the UX when the model is wrong.
What the engagement looks like
- 1–3 days a week embedded in your team, typically for one or two quarters.
- I own the product spine: user research, PRDs, prioritization, eval criteria for model-backed features, and launch.
- I translate between founders and ML engineers — the single most expensive communication gap in AI companies.
- You get a decade of product judgment (including eight years running growth product at CaaStle across a $30–50M ARR portfolio) without a $200K+ full-time hire.
Where I add the most value
Teams that have raised on a compelling AI demo and now need to turn it into something with retention. Teams whose founding engineers are brilliant but nobody owns the "why" and the "in what order." And teams about to spend three months building an agent when a well-designed workflow would ship in three weeks — I've stopped that one more than once.
The first month usually produces a sharper problem statement, an honest eval harness, and a roadmap short enough to be real.