Edgify
Edgify provides an edge-first computer vision platform that runs and trains AI models directly on in-store retail devices, enabling real-time loss prevention without cloud dependency. The platform detects barcode-switching, non-scans, and discarded items at self-checkout and back-of-house operations, with over 400 million samples processed across 2,042 stores at 98.58% precision.
- Artificial Intelligence
- Data & Analytics
- Retail Technology
- Software Only
Funding
Raised to date
$25MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
$15.5M across 2 disclosed rounds
$9M · Series A Extension
Announced Aug 2026 · from article date
Disclosed cumulative: $15.5M
Reported total $25M
Incomplete round history. $9.5M of the $25M reported total isn’t matched to a known round.
Founders
Product
Problem
Retailers face significant financial losses from shrinkage, including barcode-switching, non-scans at self-checkout, and inaccurate waste tracking. Traditional AI systems rely on heavy cloud infrastructure, constant connectivity, and centralized control, which limits their ability to make fast, reliable decisions in real-world store environments.
Solution
Edgify provides an edge-first computer vision platform that runs and learns directly on in-store devices, eliminating the need for cloud dependency or infrastructure changes. The AI trains itself and updates automatically using local data, enabling real-time detection of risky transactions and behaviors as they happen. The platform integrates seamlessly with existing retail systems, offering barcodeless product recognition, scan avoidance detection, and waste management capabilities. This always-on, in-store AI improves shrink reduction, safety, and customer experience while requiring minimal operational workload for retail teams.
Target Audience
Primary customers are retail organizations operating physical stores, particularly those with self-checkout systems, who need to reduce shrinkage and improve operational accuracy without heavy IT infrastructure.
Features
- Edge-native AI that trains and updates itself directly on in-store devices without cloud connectivity
- Barcodeless item recognition for self-checkout and self-service, eliminating false positives
- Scan avoidance detection that flags barcode-switching and non-scans in real time
- Waste management system that identifies and records discarded items in back-of-house operations
- Low-code deployment that integrates with existing retail infrastructure without hardware changes
- Privacy-first architecture that processes data locally, reducing data sovereignty concerns