Groundlight Site

Objectives:

Build an automated real-time inventory tracking using existing camera infrastructure
Minimize stock discrepancies and reduce manual inventory checks
Gain insights into customer interactions with products on shelves

Groundlight solution:

Key results:

Achieve over 90% accuracy in inventory counts using Groundlight's computer vision models
Spend less time performing manual shelf management tasks
Optimize store layout by knowing which items customers are engaging with but not purchasing

The problem: building a reliable real-time inventory tracking and monitoring system with computer vision

If you're a developer building inventory tracking solutions—whether for warehouses, manufacturing lines, or retail backrooms—you’ve probably faced the limitations of barcode scanners, RFID systems, or brittle custom vision pipelines. Groundlight provides the building blocks to create flexible, camera-based inventory monitoring using natural language and robust vision APIs.

With just a camera and a few lines of code, you can construct a computer vision system that answers domain-specific questions like:

“Are there at least 5 full crates on this shelf?”
“Is this bin missing any blue parts trays?”
“Is someone touching a box on the shelf?"

No pre-trained models to search for. No custom labeling pipeline to maintain. You define the logic, and Groundlight handles the rest.

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Why choose Groundlight for building an automated inventory monitoring system?

Camera agnostic: Groundlight works with any type of surveillance camera system, from simple USB devices to even more complex IP cameras.

Rapid Deployment: Use Groundlight's Python SDK to set up a functioning inventory monitoring system within hours.

Customizable Detection Criteria: Define specific parameters for different types of inventory.

24/7 Human Monitoring: Ensure continuous surveillance across multiple locations, adapting to various lighting conditions and environments.

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High level overview: building an automated inventory monitoring system using Groundlight's computer vision

See this github repo for more detail on how you can build this solution, tailored to your facility or store. For questions and assistance, do not hesitate to reach out with questions, application engineers at Groundlight AI are available to help.

Object detection: Precisely identify and locate products on shelves
Object counting: Accurately track the number of items for each product
Optical character recognition: Read product names into machine-readable text automatically
Analyze customer behavior: Identify when customers are touching certain products as well as returning certain products to shelves

An inventory monitoring system built using Groundlight AI’s computer vision utilizes your already-strategically placed cameras, which continuously capture images of your inventory shelves, taking only seconds to provide accurate product counts. Additional cameras can be installed if needed, yet Groundlight AI also works with many USB and WiFi cameras on the market today.

An example of how Groundlight AI is able to use Object Detection and Counting to count the number of boxes or products. Optical Character Recognition is used to recognize names of inventory, so each product is associated with its own count. You can also track which items are being touched by certain customers, and if those items are being returned to shelves.

Advantages for developers

Build a cutting-edge inventory monitoring solution, customized to your environment: Prevent revenue loss from either having too much unsellable inventory or missing sales due to product unavailability.
No extensive ML expertise required: Groundlight's platform handles the complexities of machine learning, allowing you to focus on application development.
Scalable solution: Easily expand monitoring across multiple areas or facilities without significant additional investment.
Continuous improvement: The system learns and adapts over time, improving detection accuracy with increased usage.