Packy AI

Combining AI-generated travel recommendations with deterministic user controls - keeping suggestions explainable and latency low even when model confidence varied.

Delivered a planning workflow that generates practical travel itineraries using preferences, trip context, and user constraints. The implementation balanced AI-generated suggestions with deterministic interaction patterns to keep results explainable and reliable. The product focused on maintaining low-latency responses and preserving usability even when recommendation confidence varied.

Fast iteration mattered specifically here — the recommendation UI went through many rounds of tuning against real model output, and reshaping the client quickly without a native release cycle for every experiment kept that iteration fast.

Integrating AI into a mobile experience isn't just an API call — it's a latency problem. The real challenge wasn't calling the model; it was designing an experience where the user always understood what the app was doing while it waited on a response.

I deliberately kept some AI-driven features simpler than they could have been in the first version — instead of making every interaction fully dynamic, I focused on predictable, reliable results with clear loading and fallback states.

Live on App Store and Play Store. Delivers personalized travel itineraries with fallback reliability across varying model output quality.

AI should improve the experience, not dominate it. Users care far more about responsiveness, clarity, and reliability than whether a feature is powered by the most advanced model available.