Field Research

Built on the Produce Floor

How hands-on experience as a Produce Team Leader at Giant Eagle shaped the AI technology behind Expired Solutions.

The Experience

Our founder Lawrence Hua spent 4 months working as a Produce Team Leader at Giant Eagle, gaining firsthand experience with the daily realities of produce operations — shrink, quality checks, markdown timing, and placement decisions.

During that time, he conducted a series of studies using computer vision to explore how AI could address the core challenges produce departments face every day.

4 mo

Hands-On Experience

5+

CV Experiments Conducted

130+

Produce Types Now Supported

What We Studied

Shrink Reduction

Used computer vision to identify items approaching spoilage earlier than manual inspection, enabling proactive markdowns.

Quality Assessment

Explored AI-based freshness scoring to replace subjective manual checks with consistent, data-driven quality grades.

Shelf-Life Estimation

Studied how visual indicators could predict remaining shelf life, helping staff decide when to mark down vs. when to hold.

Placement Strategy

Experimented with data-driven placement and rotation decisions — where items should go on the floor based on freshness state.

From Floor to Product

Every feature in Xpired traces back to a real problem observed on the produce floor. The AI freshness scoring, the 130+ SKU classification, and the markdown timing recommendations all started as studies during this period — and are now implemented in our production system, trained on 3M+ images.

Interested in What We Built?

See how these field insights became a production AI platform for grocery retailers.