Most data is used to explain behavior. This project used data to design it. EarliTec Diagnostics’ EarliPoint™ system relies on eye-tracking to support early autism evaluation. But it has one critical dependency: a toddler must stay engaged with a screen long enough to generate meaningful data. In reality, attention is inconsistent – especially in clinical environments – leading to incomplete datasets and delayed diagnosis. Instead of trying to change behavior, we studied it. By analyzing eye-tracking data from hundreds of diagnostic sessions, we uncovered patterns in what naturally holds a child’s gaze. Four engagement-driving traits: multicharacter interaction, rich nonverbal social cues, engaging backgrounds, and diverse focus – consistently sustained attention. We trained an AI model on these insights and applied it to more than 40 hours of Aardman animation, evaluating scenes frame by frame. But the output wasn’t a dashboard or a report. It was a playlist. A fixed sequence of scenes, engineered to hold attention, embedded directly into the diagnostic system. This was data, translated into a consistent experience that helps the system work as intended. The EarliShow shows that data doesn’t always need to be seen to be powerful. Sometimes, its greatest impact is in what it quietly enables.