RESEARCH & VALIDATION
Built on Real
Grocery Experience
Our AI freshness technology was born from hands-on produce floor research and validated through rigorous academic scrutiny at Carnegie Mellon University.
Giant Eagle Field Research
Hands-On Produce Operations Study | Pittsburgh, PA
The Problem Space
Regional grocers face persistent produce shrink challenges. Industry-wide, shrink rates average 6-8%, manual quality checks are subjective and inconsistent across shifts, and markdown timing is often reactive rather than data-driven.
- • 6-8% industry average shrink rate on produce
- • 2-4 hours daily on manual quality checks
- • Inconsistent markdown decisions across shifts
- • Emerging regulatory pressure (CA AB 660)
Our Research
Our founder spent 4 months working as a Produce Team Leader at Giant Eagle, studying operations firsthand and conducting computer vision experiments to address the challenges he observed every day on the floor.
- ✓ CV-based shrink detection and early spoilage identification
- ✓ AI freshness scoring to replace subjective manual checks
- ✓ Shelf-life estimation for proactive markdown timing
- ✓ Data-driven placement and rotation strategies
What This Research Became
Every feature in Xpired traces directly back to a problem observed on the produce floor. The studies conducted during this period shaped the product that exists today.
94%
Produce ID accuracy
135
Produce types supported
<30ms
On-device inference
EfficientNet
B4 architecture
Technical Validation
Carnegie Mellon University
McGinnis Venture Competition
Expired Solutions was selected as a McGinnis Venture Competition Finalist, validating both the technical approach and business model through rigorous scrutiny by CMU faculty and industry experts.
Dual Neural Network Architecture
TwoModelService combining ProduceClassifier (EfficientNet-B4, 135 types) and FreshnessClassifier (EfficientNet-B4) for classification and quality scoring.
CoreML On-Device Inference
Models converted to CoreML for real-time on-device inference on iOS, with server-side PyTorch backend for full-resolution analysis.
GPT-4o Fallback
Budget-controlled GPT-4 Vision fallback for edge cases where the custom model confidence is low, ensuring robust coverage across all produce types.
Estimate Your Savings
See how much your stores could save with AI-powered freshness scoring. Our ROI calculator uses industry data from USDA, ReFED, and EPA.
Try the ROI CalculatorInterested in What We're Building?
We're looking for grocery retail partners to bring AI-powered freshness management to their produce departments.