The AI install pack goes into the AI you already use, then paste in your North Star and ask what keeps it honest. It coaches the two things a single headline number needs and the average AI forgets: an input tree and guardrails.
A North Star metric does one valuable thing: it points a whole company at the single number that best proxies delivered customer value. Focus is the point, and a well-chosen North Star creates it, aligning teams that would otherwise optimise in different directions.
A single number invites gaming, and the average LLM rarely warns you about the inputs and guardrails that keep a North Star honest. Left alone, a North Star becomes a target that gets hit while value quietly falls. The upgrade builds the input tree beneath it and the guardrails around it.
A North Star is moved by a small set of input metrics, not by willpower. Name them: the three or four levers that, if they move, move the star. Teams act on inputs they can influence this week, not on a lagging headline they can only watch.
For every North Star, name the metrics that must not get worse while you chase it. A support team can lift "weekly active users" by spamming notifications, right up until retention and satisfaction collapse. Guardrails are what stop a win on paper being a loss in reality.
Revenue is the result of delivering value, not the value itself. A North Star built on customer behaviour, the action that means someone got what they came for, leads revenue. One built on revenue lags it and tempts short-term extraction.
The before is a lagging result that invites extraction. The after names customer value, the inputs a team can move this week, and the guardrail that stops the number being gamed, which is the whole difference between a focusing metric and a vanity one.
Same files, three tools. Pick yours. It really is about two minutes.
Before you install, ask your AI to review one real goal and screenshot the answer. Install, ask again, screenshot the difference. That gap is the point. Try: Tell your AI your North Star metric and ask: what are the three or four input metrics that move it, and what guardrail should stop us gaming it?
The AI install pack comes with From Trading to Scaling: an hour to read, five working templates, the instructions and knowledge files for Claude, ChatGPT or Copilot, and a conversation with Matt about any of it.
Free things get downloaded, not installed. A hundred pounds is the smallest number that makes somebody open the file, and this is the only place the pack is available.
The pack carries the principles and the working templates: the book, the strategy one-pager, the OKR template, the idea sheet and the bonus calculator, plus the knowledge files that make your AI coach against a method rather than the internet average. That is yours to keep, and it is enough to install all six things without us.
What it is not is the whole machinery. The playbooks, scoring scales and the 140-plus linked concepts, installed into your own tools and kept current, are the licensed layer above this. And the human work, the training, facilitation, coaching and mentoring that gets a company past its first messy quarter, is our people. Knowledge does not install courage, or referee a hard conversation. We would rather say that here than let you find out.
// one install, or the whole companyOne install helps one person. The advantage is your whole company running on a shared operating system: the same method in the AI everyone already uses, at every level, with routines and automations carrying the repeatable work, and your people freed to find and compound the knowledge and IP only you have.
One install at a time does not create that. Building the operating system does, and that is what ZOKRI does. See the method we install and the platform it runs on.
The single metric that best captures the core value your product delivers to customers, used to align a whole company on one direction. Good ones measure customer behaviour, not revenue, because behaviour leads revenue.
On its own, a single number invites gaming: teams can move it while the value it was meant to represent falls. It needs an input tree (the levers that move it) and guardrails (the metrics that must not get worse), or focus curdles into tunnel vision.
They fit together. The North Star sets the long-run direction; OKRs move the input metrics beneath it, one quarter at a time. Use the North Star to choose which input to push, then write the OKR that pushes it.