We build the uneven advantage in AI for mid-sized global companies.
A 30-day path from diagnosis to production. Then a long runway of operating leverage.
We map your value chain against current AI capability. Where is compounding leverage hiding — and where is the organization well-defended against change? We leave with a ranked portfolio of bets and an honest readout on readiness.
- Value-chain & data audit
- Capability & readiness map
- Opportunity portfolio, ranked by ROI × feasibility
We pick one bet and build it end-to-end with your team. Not a notebook, not a demo — a production system with evals, observability, and a human in the loop where it matters. One team, one problem, one working thing.
- Reference architecture & model selection
- Evals, guardrails, observability
- Rollout plan & operator training
One win is a project. A platform is a practice. We build the internal substrate — data, infra, governance, patterns — so your next five AI systems ship in weeks, not quarters, without us in the room.
- Data & prompt governance
- Shared infra: RAG, agents, evals, cost controls
- Internal playbook & hiring plan
Optional: we stay embedded as a fractional AI office, running the platform, training new hires, and underwriting the next wave of bets. When you don’t need us, we leave quietly and well-documented.
- Fractional AI leadership
- Quarterly portfolio review
- Red-team & safety reviews
Six rules we won’t negotiate on. They’re the reason the work ships.
Ship systems, not slides.
If it isn’t in production with real users, it doesn’t count. Every engagement has running software by week six.
Build with you, not around you.
Your team is in the repo, in the standup, in the evals. We work ourselves out of the job — that’s the job.
Evals before eloquence.
A demo is a trick. An eval is a promise. We invest in measurement first, so every change is defensible.
Small teams, short loops.
Two operators, one engineer, one researcher. Weekly releases. Nothing about this scales by adding headcount.
The P&L is the oracle.
We instrument for business outcomes from day one. Latency, accuracy, and cost are only meaningful in dollars.
Leave it documented.
We write runbooks, eval suites, and onboarding docs as we go. The last deliverable is an org that doesn’t need us.
Built by tech founders who have scaled companies. Forged at Stanford and McKinsey, with an operator’s bias.
Asymmetry was founded by tech operators who have built and scaled companies before advising anyone else to do the same. We pair that operator instinct with training from Stanford and a background at McKinsey — working out of the Bay Area, at the epicenter of where AI is actually being built.
- 01Tech founders with proven scale
- 02Based at the AI epicenter, Silicon Valley
- 03Trained at Stanford
- 04Advisory roots at McKinsey & Company
- 05Shipped AI systems in production