← Cases
United StatesRetail and e-commerce2023 – ongoing

Sprouts Farmers Market

AI early leak detection for store refrigeration

Sprouts' AI leak detection flagged 21 refrigerant leaks that its existing sensor-based method missed in a nine-month test

WorkingVendor case study

Problem

Refrigerant leaks in store refrigeration systems are costly and often found late. Sprouts relied on a sensor-based maintenance service to catch them.

Approach

Sprouts tested Axiom Cloud's AI-driven Early Leak Detection module at 115 California stores from April to December 2023. The test ran head to head against the existing sensor-based method, which the source calls the Alternative.

Results

21 leaksleaks found by Axiom that the existing method missed completely, per Axiom CloudVendor case study[1]
47 daysaverage lead time by which Axiom flagged the 11 leaks that both methods caught, per Axiom CloudVendor case study[1]
115 storesCalifornia Sprouts stores in the April to December 2023 testVendor case study[1]

Takeaway

A side-by-side test against the incumbent method gave Sprouts a direct comparison of both detection rate and timing. The figures come from the AI vendor.

Categories

Business function
Production and quality
Innovation type
Process innovation
Evidence
Vendor case study

Sources

Source summaries

[1]Refindustry7 Jul 2024

The trade outlet reports a nine-month test of Axiom Cloud's AI leak detection at 115 Sprouts stores in California. Axiom found 21 leaks the sensor-based method missed and flagged the 11 shared leaks 47 days earlier on average. The figures are Axiom's.

Full details are in the original sources.

Added 2 Nov 2026 · Updated 2 Nov 2026
Open case page ↗