This site
CapiraLabs.com is itself an experiment: a local-first measurement foundation (events and leads in the browser, no third-party analytics) that we'll grow only when the data says to.
What we do in the lab
Experiments, not initiatives.
The lab is where we test whether an idea deserves to become a system. Product features, marketing machinery, the structure of the AI-associate org itself — everything enters as a hypothesis and has to survive the loop before it gets more investment.
The experiment loop
Four steps, no shortcuts.
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Hypothesis
State what we believe, in a sentence that can be wrong. "Owners will update a monthly worksheet if it takes under ten minutes" is a hypothesis. "Engagement will improve" is not.
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Metric
Decide what number would prove or kill the hypothesis before building. If we can't name the metric and the threshold, the experiment isn't ready to run.
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Result
Run it small and read the number honestly. A negative result is a result — it closes a door cheaply, which is the point of running experiments at all.
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Next decision
Ship it, revise it, or stop. Every loop ends in a decision someone owns, recorded where the next experiment can build on it.
Currently in the loop
What the loop is chewing on.
AI-associate org design
How many associates, in which roles, with what review chains? Arishem gives us the data — review iterations, ratings, misjudgments — to answer it empirically.
Product coaching loops
Inside Capira360, the AI CFO Coach is being tested the same way: grounded guidance against real worksheet numbers, measured against whether owners act on it.