AI product management services

Strategy, scoping, and delivery for AI products — from a product manager who builds RAG pipelines and agents himself, not one who read about them.

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There's a new gap in product organizations: teams have LLM ambitions, engineers experimenting with models, and executives asking "what's our AI strategy" — but nobody owning the product judgment in between. Which use cases justify probabilistic software? What does "good enough to ship" mean when outputs vary? Who decides what happens when the model is wrong? That ownership is what I provide.

A PM who ships AI, not just decks about it

I co-founded WisOwl AI and built its core systems personally: an embedding-based semantic matching engine on FAISS and Supabase pgvector, and autonomous recruiter agents matching supply and demand in the Indian hiring market in real time. The platform has 5,000+ organic signups and 15+ recruiter partnerships with zero paid marketing. Before that, eight years at CaaStle running consumer experience and growth product across a $30M–$50M ARR portfolio — so the AI work I do always answers to retention and revenue, not demo applause.

The services

  • AI product strategy: a candid map of where LLMs create real leverage in your product versus where they add cost and risk — with build-versus-API decisions, model selection logic, and unit economics attached.
  • RAG and search products: scoping and product-managing retrieval systems — corpus strategy, retrieval evaluation, and the UX for the queries retrieval can't save.
  • Agentic products: deciding when autonomy is justified, designing guardrails and human-in-the-loop checkpoints, and defining evals before the build rather than after the incident.
  • AI feature delivery: embedded fractional product leadership — 1 to 3 days a week — running your AI roadmap from PRD through launch, including the evaluation harnesses that make quality a measurement instead of a mood.
  • AI product audit: a one-to-two-week review of an existing AI build: retrieval quality, eval coverage, failure UX, latency and token economics, ending in a ranked fix list.

The spoke pages linked below go deeper on each. If you already know which problem you have, book the call and skip the reading.

Frequently asked questions

What makes AI product management different from regular PM work?
Determinism disappears. Specs become evals, QA becomes statistics, edge cases become the roadmap, and cost scales with usage. A PM who treats an LLM feature like a CRUD feature ships something that demos well and decays in production.
Do we need a dedicated AI PM or can our existing PMs learn?
Your PMs can learn — and part of my engagements is teaching them. Most teams need senior AI product judgment now and internal capability in six months; fractional bridges exactly that gap without a speculative $200K hire.
Which models and stacks do you work with?
Production experience with FAISS, Supabase pgvector, embedding pipelines, and the major LLM APIs, plus orchestration frameworks. I'm deliberately vendor-neutral: the eval harness and the economics pick the model, not the logo.
Can you help us decide if an AI feature is even worth building?
That's often the highest-value engagement I run — a short scoping sprint that prices the feature honestly (build cost, token economics, failure risk) against its value. "Don't build it, do this simpler thing" is a common and happy outcome.

Related pages

Let's talk about what you're building.

Always happy to chat with founders, builders, and growth operators. 30-minute introductory call. No agenda needed.

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