FOR HEADS OF AI · FRACTIONAL CAIO ADVISORY
Fractional Head of AI advisory for Series B through pre-IPO teams. Roadmap pressure-testing, eval harness engineering, and embedded review on the architecture decisions that lock you in.
The research says
88%
of AI pilots never reach wide deployment. Not because the model failed — because the harness, the evals, and the behavior design weren't there.
Your board spent 2024 asking “what's your AI strategy?” They're now asking “what did it cost, what did it return, and how do you know?” You're the only person who can answer both questions — and there's no peer group, no precedent, and the clock is running.
You have pilots in purgatory, an eval harness that's still a spreadsheet, and an agentic loop that works in the demo and falls apart on angry customers with ambiguous requests. The model is fine. Everything around it isn't.
Nobody inside the org can bridge technical depth and board communication simultaneously. That's your job — and you're doing it alone.
What I do with AI leaders
Your roadmap is a bet. Most AI roadmaps are under-specified on eval infrastructure and over-indexed on model capability. We go line by line — what's grounded in production reality, what's wishful thinking, what needs to be cut before it burns a quarter.
If your evals aren't catching the failures your customers are finding, they're not evals — they're theater. I build eval harnesses from production logs, not from a room full of guesses. And I model inference cost before it surprises the board.
When your team is making the decisions that lock you in for 12 months, a solo AI leader shouldn't be the only voice in the room. I sit on the architecture calls, pressure-test the tradeoffs, and help you defend the choices downstream.
From the field
Series B fintech · Q4 2025
Problem
Inference costs tripled month-over-month. Engineering couldn't trace which model calls were driving the spike. Board was asking CFO questions the team couldn't answer.
Outcome
Inference cost reduced 58% while throughput increased. First time the team could answer a board question about cost-per-transaction.
Consumer marketplace · Q1 2026
Problem
Two years of AI feature work, one agent in production, zero eval framework. Releases were manual spot-checks by a PM with a spreadsheet.
Outcome
Eval cycle dropped from 2 weeks manual to 3 hours automated. Next model upgrade shipped in one sprint instead of a quarter.
Pre-IPO enterprise SaaS · Q2 2026
Problem
No peer inside the org to pressure-test AI architecture decisions. Board pressure accelerating. AI roadmap built on demo-layer assumptions that hadn't held in production.
Outcome
Rebuilt the roadmap around shipped capabilities. Delivered board narrative grounded in production evidence, not pilot aspirations.
Selective by necessity — I work with a few teams at a time. If there's real fit, we figure that out together — not through a sales process.