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Engagement · Agentic Product

For product management and product teams.

From writing the specs to directing the agents.

We build a shared AI system into how your product team works, from research and discovery to specs and prototypes, and train them to run it, so you validate more ideas and ship better product without growing headcount.

Book a call 30 minutes.
01 · The problem

You bought the AI tools. Your product team still moves at the same speed.

Every product leader is under the same pressure: validate more ideas, make better calls, and ship better product, without adding headcount. So you rolled out the AI tools. A few of your PMs got dramatically faster at drafting and summarizing. But how quickly the team actually goes from a raw idea to a validated decision has barely moved.

The reason is that AI landed as an individual tool, not a team capability. Adoption is a barbell. Your strongest PMs have gone deep and your juniors love it, while the people in the middle treat it as a better search box. Everyone works differently, with their own prompts and habits, and nothing they learn is shared. The research one PM synthesizes on Monday stays in their doc. A prompt that saves someone an hour is invisible to everyone else. Nothing compounds.

This is the wall most product teams hit, and it is not a tools problem. The frontier models are already good enough. The hard part is the operating model: how discovery gets run, how specs get written, how prototypes get built and validated, and how every insight gets captured so the whole team has it. That is what we build.

02 · What we build

One operating model, and the team trained to run it.

Two halves that work together. A system built into how your product team works that turns scattered AI use into one shared way of working, and the coaching that gets everyone running it, including the skeptics.

Half one

The system, built into how your team works.

Every useful thing your team learns about working with agents goes into one place, shared across the product team, where everyone draws from it and adds to it. Concretely, that means:

  • Context agents work from Structured knowledge of your product, customers, market, past decisions, and data sources, so an agent works from how your business actually operates instead of guessing.
  • The work of product, as skills Research synthesis, PRDs and specs, competitive teardowns, prototypes, and experiment design, reusable by everyone instead of re-invented by each person.
  • One way to run discovery and definition A consistent way to synthesize research, write a spec, size an opportunity, and decide what to build, so output is predictable no matter who started it.
  • Guardrails, and a human on every call Accuracy and quality rules enforced in the system, not left to trust, so a person owns and signs off on every artifact and every decision.
  • Discovery that runs in the background Research synthesis, competitive monitoring, and feedback triage running continuously, so the team comes in to synthesized insight instead of a pile of raw inputs.

A person owns and signs off on every artifact and every decision, and every improvement is shared, so the gains belong to the whole team, not one laptop.

Half two

Everyone trained to run it, and to improve it.

A shared system only works if people actually use it, so the training runs on your real product and the operating model we set up, not generic AI lessons.

We bring the whole product team to a common level first, so everyone shares a language and the same baseline habits. Then we push past basic usage to the real unlock: getting people to contribute back. When someone builds a new skill or captures new context, the whole team gets better. That is the difference between a team that consumes AI and one that compounds with it.

We pay special attention to the people who are stuck or skeptical, mapping each one to a problem they personally care about. The fastest doubter to convert is usually the one who sees an agent solve their own hardest problem in an afternoon.

03 · The work it covers

One system across your product process.

The agents and workflows cover the product process end to end, so the whole team works the same way from first insight to shipped feature.

04 · The end result

What the work looks like once the system runs.

Once the system is running, the shape of the work changes.

PMs direct, not draft

Your product managers stop hand-synthesizing research, writing specs from scratch, and building analyses by hand. They direct agents through that work and spend their time on what needs a human: deciding what to build and confirming it is right.

Discovery runs in the background

Research synthesis, competitive monitoring, and feedback triage run continuously, so the team walks in to synthesized insight and open questions, not a backlog of raw inputs.

AI is how the team works

The result is a product team that validates more ideas, kills the wrong ones sooner, and ships better product faster, without growing headcount.

05 · How the engagement works

A pilot first, then scale.

We start with a pilot inside your product team, focused on two or three high-value workflows, like idea to validated spec, discovery to decision, or feature launch to adoption. It runs in three stages inside a 6 to 8 week window, then scales across the rest of the team once it proves out on its numbers.

i

Assessment · about 2 weeks

We interview the team, map how work flows today from discovery to shipped feature and where it slows down, and capture the baselines we will track. You get a prioritized roadmap: the specific workflows worth building first, mapped to your actual team, tools, and data.

ii

Implementation and activation · about 4 to 6 weeks, in parallel

We build the operating model and its highest-value pieces, then train the team alongside the build so the system is in use by the time we step back. Concretely, you get:

  • 3 to 5 reusable, end-to-end product workflows with documented playbooks, across discovery, spec, and validation.
  • 1 to 2 prototype AI agents, plus a shared prompt library and configured model usage.
  • PRD and spec templates, research-synthesis frameworks, and experimentation playbooks.
  • The ownership model and AI governance guidelines, with KPI tracking, agent monitoring, and guardrails built in.
iii

Hypercare

After the system is live, we stay on through the first stretch of daily use, refining the setup and supporting adoption while the new way of working becomes the normal way of working.

06 · Who it is for

Built for product teams that want to ship better product faster, not hire more.

For product management and product teams. It is a fit if:

  • 01 Your product team already uses AI, or you are behind and want to catch up fast.
  • 02 Usage is scattered and individual, and you want a system the whole product team runs on.
  • 03 You have a leader who can commit the product team to a shared way of working.

It works alongside your existing product process and stack. It is not a generic AI course and not a product-management course. It is an operating model for how your team actually does the work, grounded in your real product and measured by outcomes.

07 · Common questions

The questions teams ask first.

Is this about replacing PMs?

No. The goal is more, and better-validated, product from the team you have. Your people move from drafting specs and synthesizing research by hand to directing agents through that work, and the human parts, product judgment and confirming the work is right, become more important, not less.

Won't AI-drafted specs and prototypes be low quality?

A human reviews and owns every artifact. The agents are built on your context and standards, guardrails are enforced in the system, and nothing ships unseen. The point is a faster first draft to react to and a cheaper prototype to test, not work that goes out unchecked.

How is this different from just giving everyone ChatGPT?

That is a single-player tool that improves one person's productivity. This is a system for the whole product team: shared context, skills, and standards that everyone draws from and adds to, so every improvement compounds across the team instead of staying on one laptop.

Does the team still own product decisions?

Emphatically, yes. Agents do the production work, the synthesis, the drafts, the prototypes. A human makes and owns every call about what to build and whether it is right. The judgment stays with your team; the busywork does not.

How do you handle our data and any compliance requirements?

The setup uses enterprise agreements with no data retention and no training on your data, with self-hosted and private options where needed. We define accuracy, quality, and human-in-the-loop standards up front, and shape them to your compliance context, especially for teams in regulated environments.

How do you measure whether it is working?

We capture baselines during the assessment across workflow efficiency, product impact, and adoption, and track them through the engagement. The assessment also produces a quick-wins list that tends to pay for the engagement on its own.

How disruptive is this day to day?

It works alongside how your team already operates. There is no big switchover. The team picks up a shared language and a few light habits, and the new way of working settles in from there.

How fast will we see results?

The pilot runs in a 6 to 8 week window: about two weeks of assessment, then four to six of implementation and activation in parallel. Meaningful movement on cycle time, rework, and experimentation velocity shows up over the first weeks of daily use and compounds from there.

08 · Talk to us

Ready to make AI how your product team works?

Start with a pilot: an assessment of where your product team is today, and the fastest path to a system the whole team runs on, from discovery to shipped. 30 minutes is enough to know whether it fits.

Book a call Or write to us at [email protected]