AI product manager
Krine, an AI product manager that knows where to stop.
It runs the convergent product work and hands back the calls that need a human.
What is Krine?
Krine is an AI product manager built by RampStack. It does the convergent product work, research, specs, roadmaps, and experiment analysis, on its own, and stops at the decisions that need human judgment, surfacing them as clear choices rather than making them.
The idea
Two kinds of work, kept separate.
Some product work has a knowable-correct answer and a known process to reach it. Writing a spec from a decided feature. Computing a sample size. Ranking a backlog against stated inputs. Krine runs this work.
Other product work has no knowable-correct answer, only judgment, taste, and stakes. Which positioning to pursue. Whether to ship or kill. Krine stops here and hands the decision back, already researched and framed as a clear choice.
What Krine does
Five jobs, each with an honest stop built in.
Conversion testing.
Reads a funnel, generates ranked, evidence-grounded test ideas, and stops at which one to run. When the data is too thin to support a trustworthy test, it declines and says why.
Feature definition.
Turns a decided feature into a complete spec, and stops rather than write a spec around a missing answer.
Roadmap planning.
Ranks a backlog within each theme, maps dependencies, fits the plan to capacity, and surfaces it as a recommendation. It does not set the strategy.
Readiness check.
Confirms there is a defined surface, a set goal, and enough data before a run starts, and declines legibly when something is missing.
Experiment evaluation.
Evaluates a measured result and never calls a winner before it is statistically significant. A large lift on a small sample is reported as still accruing.
The stop
The honest stop is the point.
Krine does the work, and the stop is where it hands you the decision. An agent earns trust by being honest about its boundary, and the boundary is where the product work meets the decisions only a person should own.
How Krine works.
Krine is the decision engine. Give it the work that competes for attention, a backlog, a set of ideas, the fallout from an incident, and it decides what proceeds, what needs evidence first, and what stops at a person.
The core of it is a mode classifier, the idea the RampStack whitepaper calls the bimodal split. Convergent work has a defined target and a verifiable finish: it can be specified, handed forward, and checked. Divergent work is a judgment call: the target itself is in question, the decision is hard to reverse, or reasonable people would choose differently. Krine classifies each item before anything else happens, because the two kinds of work need opposite handling. Convergent work moves; divergent work earns a person's attention, framed well.
Krine also keeps state. Decisions, the rationale behind them, the evidence they rested on, and what happened after are recorded, so the next decision starts from the last one instead of from zero. And it reports its stop rate, the share of items it declined to decide alone, as a first-class number. A decision engine that never stops is not deciding, it is rubber-stamping, and the stop rate is how you can tell the difference.
A worked example
A five-item backlog.
Input: five items from a product backlog, the kind that pile up in any planning doc.
What Krine decides
"Add CSV export to the reports page."
ProceedConvergent. The target is defined and the finish is verifiable. It leaves as a prepared goal, with pm-spec-writing turning the one-liner into a spec an agent or a person can build from.
"Checkout conversion dropped 12 percent this month."
Evidence firstNot decidable yet, in either direction. analytics-strategy and journey-mapping locate where in the funnel the drop lives, and the item returns to Krine with findings attached.
"Redesign the pricing page."
Stops at a personDivergent. Pricing touches positioning and revenue, and a wrong move is expensive to walk back. Krine frames the question, attaches what the evidence skills found (cro-optimization, competitor-experience-audit), and waits.
"Two onboarding flows, pick one."
ProceedDecidable by experiment. experiment-design and usability-testing define the test and the metric; the result makes the call. If the result comes back ambiguous, it stops at a person rather than torturing the data into an answer.
"Sunset the legacy dashboard."
Stops at a personDivergent and irreversible: users lose something. A person decides, with the usage evidence attached and stakeholder-communication queued for whichever way the call goes.
The tally: two items proceed (one straight to a spec, one through an experiment), one goes to evidence-gathering, and two stop at a person.
Stop rate
two of five
Krine reports that number with the plan, because the stops are the decisions that mattered most.
What the example shows.
Five items went in and none of them got the same treatment, because they were not the same kind of work. The convergent items moved without ceremony. The ambiguous one was refused until there was evidence. The two that were strategic or irreversible were stopped, framed, and handed to a person with the homework already done. The catalog supplies the skills that specify, measure, test, and communicate; Krine supplies the judgment about which items deserve which, and the honesty to say which decisions were never its to make.
Read the framework. Read the code.
The whitepaper is the thinking. The repo is the working implementation. Solutions is how to bring RampStack in to operate it for you.