Bring ProductHarness to Your Product Org
A one-day working session that shows your product org what AI-first product work looks like in practice — and an optional 30-day installation that makes it stick.
One Day. Then a Decision.
The engagement is structured so the commitment matches the evidence. Day One is a self-contained working session: your team sees the model, watches a feature from your domain prototyped live, and leaves with a running harness and an adoption map. If the org wants the capability installed — not just demonstrated — the 30-day installation is the next step. It's scoped at the end of Day One, against what the room actually responded to.
The Whole Product Org — and Engineering Leadership
The session is built for product organizations whose engineering teams have adopted AI coding agents while product process hasn't changed: PMs still hand off speculative documents, agents faithfully implement them, and the gap widens.
In the room: product managers, UX designers, and QA — the roles whose work the framework redefines. Also in the room, deliberately: engineering leadership. The framework's output lands in engineering's codebase and culture. Adoption fails when engineering first encounters it as a surprise. They are part of the audience, not an afterthought.
A product org trained without engineering in the room produces enthusiastic PMs whose output engineering won't accept. Both sides of the handoff are in the session — that's a design decision, not a logistics one.
Intake and Security Review
Two things happen before anyone walks into the room, so the session runs on your reality instead of generic examples.
A questionnaire covering your stack, source control, conventions, backlog tooling, and current requirements practice. It's what lets the live demonstration prototype a feature shaped like your product — not a generic to-do app — and lets the ProductHarness specification be tailored to your standards before Day One, so the harness your agent generates on the day is yours from the first commit.
The framework puts requirements, standards, and prototype code within an AI agent's context. Your security organization will have questions about that — which models, what data residency, what the agent reads and transmits. Those questions get answered in writing before the session, on your security team's terms. For a security company especially: this conversation happens first, not after. The full posture document — architecture, data flows, threat model, and vendor verification checklist — is here: AI Security Posture.
The Working Session
Not a slide presentation with a demo at the end. The day is built around watching the model run on your domain, then mapping what your org can consume first.
What changed when AI coding agents became standard in engineering, why product process didn't follow, and what the translation tax costs a product org. The framework's four principles — including the one that matters most to leadership: accountability stays with people.
A working prototype generated live, in the developer environment, grounded in steering documents configured from your intake. Stakeholder-style feedback on working behavior, iteration in the same session, then requirements and acceptance criteria derived from what was validated. The double loop, compressed into a morning, on a problem your PMs recognize.
PM, UX, QA, and engineering each see their workflow in the new model: what they stop doing, what they start owning, and where the judgment work concentrates. Built from the framework's role definitions, applied to your org structure.
Working session against the adoption path: which capabilities your org can consume now with behavioral change alone, which require data and process maturity, and which need sustained culture work. The output is a sequenced map, not a maturity score.
Thirty minutes with product and engineering leadership: what the room responded to, where the resistance will come from, what the 30-day installation would target, and whether it's warranted.
Artifacts, Not Inspiration
The harness is generated from the ProductHarness specification — tailored to your standards during intake — by your own approved agent, inside your own source control, during the session. Yours from the first commit; nothing cloned from outside. Not a trial, not a sandbox.
Which capabilities to consume first, which to defer, and what each one requires of the org — produced with your team in the room, owned by your leadership after.
Concrete, measurable outcomes the org could pursue next (for example: a set number of PMs taking a real backlog item through the build-validate-derive loop). If the installation happens, these are its success criteria. If it doesn't, they're yours to run with.
The Optional 30-Day Installation
A day creates understanding. It doesn't create habit. The installation is four weeks of embedded working sessions that take a real team from "saw it work" to "works this way" — against the targets defined on Day One.
Real work in the harness
A pilot team takes live backlog items through the loop — prototypes built, validated, requirements derived — with hands-on working sessions, not check-in calls.
The org around the team
Downstream distribution wired to your backlog and test tooling. Engineering review of PM-generated output, on engineering's terms. Steering document governance stood up with named owners.
Measure and hand off
Results against the Day One targets, honestly reported. A leadership readout on what took hold, what didn't, and what the org should do next — with or without me.
What It Costs
The 30-day installation is scoped and priced at the Day One leadership debrief, based on pilot team size and the targets the org commits to. It is never a condition of Day One — the session is built to stand alone.
What This Is Not
There is no software to buy, license, or host. The framework runs on the AI coding agent your org has already approved. What the engagement installs is practice and structure, not product.
The framework's premise is human differentiation, not human replacement: move the mechanical work to the agent so every person spends more time on the work only they can do. If you're shopping for a reduction-in-force rationale, this is the wrong room.
Prototypes are explicitly non-production artifacts. Anything that moves toward production passes through engineering's existing review, branch protection, and CI — engineering owns implementation soundness, before and after the harness.
ProductHarness is a working model refined in practice, with an honest, published list of open problems. What your org gets out of it depends on conditions only your org controls — which is exactly why Day One ends with defined targets instead of promises.
Start with the Intake
One email starts it. You'll get the intake questionnaire and the AI-posture document for your security team within two business days, and we'll hold a date for the session.