> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pioneeroptimisation.com/llms.txt
> Use this file to discover all available pages before exploring further.

# How PIO optimises

> Baseline first, learned models as they prove out.

Every project starts safe. A baseline model represents your game as it is: control-group players get your default actions, and the experiment has a control worth trusting.

Learned models train on the decisions and valued events your game sends. They take traffic only after passing readiness gates: enough decisions, events, and players observed, against thresholds set by policy. Until a model is ready, it cannot be activated; the dashboard shows each unmet requirement with its observed and required counts, and what to do next.

Traffic then shifts deliberately, not automatically to 100%. The control group persists so results stay measurable, which is why your code always handles the baseline case: a null `Action` is the control arm, not an error.

The numbers you read in the dashboard carry the same discipline. A metric without enough data behind it is annotated as not yet meaningful rather than presented as a result.


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