The Brief

Calorie and fasting-tracking apps are one of the most saturated categories on Google Play, and most of them lose users within weeks. The pattern is almost always the same: logging food is tedious, and tedious things get abandoned. The brief for Kalori AI was self-directed — build a nutrition and fasting assistant where the app does the interpreting, not the user typing macros by hand every meal.

The Approach

The build used AI-assisted scaffolding and iteration while focusing on the core nutrition and fasting workflow. The product work was kept distinct from the development tools: the useful outcome is helping a user log and understand their habits, not simply generating code faster.

Where AI Fit In

Inside the product, AI is used to turn raw logging input into something closer to a personalized nutrition and fasting insight, rather than a static number on a dashboard. The engineering judgment — what to track, what to surface, and where the app should stay simple instead of adding another settings screen — stayed with the developer; AI accelerated the implementation, not the product decisions.

The Outcome

Kalori AI is published on Google Play under the Zaidan Lab developer account. It provides an inspectable example of AI applied to a consumer workflow, rather than a claim about client results, adoption or health outcomes.