Computer Vision for Retail

Using computer vision technology we tracks products on retail shelves.

We verify product availability, quantity, and correct placement.

And all of this without human intervention.

WORKFLOW

Photos

Cameras capture images of the shelves on a scheduled basis. The system sends the collected images for processing.

Processing

Machine learning algorithms detect products in the images and match each one with the internal product database.

Results

Reports and alerts are generated. If something is wrong, the system immediately notifies you.

How to Choose Cameras for Retail Shelf Recognition | A Guide

Resolution, field of view, capture frequency, and lighting — a practical guide to choosing cameras for automating shelf and price tag monitoring in retail stores.

WHAT IT IS FOR

With an automated shelf monitoring system, you always know the current state of product placement. All that’s left for you is to take action.

Here's what you get as a result:

Fewer losses from empty shelves. The system immediately alerts you when products are running low.
Planogram compliance control. Easily check whether brands and flavors are placed correctly and whether merchandising guidelines are being followed.
Staff time savings. Employees no longer need to manually inspect shelves with checklists, — everything is monitored automatically. If the system detects an issue, you’ll be notified immediately.
Objective insights for management. Gain accurate, data-driven visibility into what’s happening in your store.
24/7 operation. The system runs non-stop, without breaks or days off.

Возможности применения

Automating Price Tag and Promo Compliance in Retail | Product Price Verification

An automated system for monitoring price tags and promotional displays in stores. Detects price mismatches, layout errors, and helps reduce fines and customer complaints. Pilot and rollout across store networks.

Planogram Compliance in Retail | Automation of Shelf Organisation Plan

An automated planogram monitoring system: detects layout violations, empty shelves, and promo non-compliance. Real-time analytics across the entire store network.

STAGES OF INTEGRATION

1. Test Launch (2–4 weeks)
First, we launch the system on a small number of stores or even a single shelf.
Our main goal is to understand the specifics of implementing the system in real-world conditions. We check, if:

Cameras are performing well enough.
Products are clearly visible as required.
Shadows, reflections, or unusual layouts interfere with detection.

At this stage, we honestly measure the initial performance metrics: how many products are identified correctly, what errors occur, and how quickly the data is updated.

2. Pilot Phase (1–2 months)
If the first stage is successful, the system is rolled out across several stores and often times with different assortments, layouts, and shelf types.
At this stage, it is important to:

Test how robust the system is under varying conditions;
Work through complex real-world scenarios (such as new products, non-standard displays, or heavy customer traffic);
Establish processes for handling cases that require human intervention.

During this phase, we not only validate the technology but also identify operational nuances: how quickly staff respond to system alerts, whether false alarms cause fatigue, and whether there are measurable improvements in operations.

3. Scaling
After a successful pilot, the system is rolled out across the entire network. We set up monitoring, regular data updates, and ongoing feedback collection.

What makes it different from other recognition systems?

Our system sees better and more precisely than traditional object recognition solutions.

Most recognition systems can distinguish between a can and a box.
Our system goes much further:

Identifies specific beverage brands.
Recognizes individual yogurt flavors.
Differentiates between various packaging types of the same mayonnaise product.
Detects product size or weight (120 g or 180 g of coffee?).

Stores typically carry dozens of very similar products, and the assortment changes almost daily. Our system can accurately distinguish between them, even when the packaging looks nearly identical.

YOU WILL ALWAYS BE AWARE:

Are the products in stock?
Are brands and flavors placed correctly?
Is the planogram being followed?
Are there placement errors, empty spaces, or compliance issues?

All of this without waiting for scheduled store walks or manual inspections.

WHO IT IS FOR

Retailers
FMCG companies
Distributors
Brands
Analytics teams
Operational control departments

KEEP IN MIND

The system takes over about 95% of routine tasks.

However, some cases still require manual review:

new products without reference images;
items blocked or covered by other packaging;
rare errors or edge cases, etc.

This is normal practice. Artificial intelligence handles the majority of the work, while humans step in to manage non-standard situations.

Why Recognition Models Deliver Imperfect Results in Real Life | CV Quality Metrics

Why 95% accuracy doesn’t guarantee good performance in-store. A breakdown of real causes of product recognition errors and the business metrics that actually matter.

Errors in Product Recognition Systems: Causes and How to Fix Them

Why computer vision systems make mistakes and what to do about it. Lighting, cameras, data, and model retraining — a practical breakdown for retail.

Ready to Discuss?

If you’re interested in our product recognition system or have any questions, please get in touch with us.

We’ll help you determine whether this technology is right for your network and what to expect from implementation.

We’ll share our experience and discuss your specific stores, including what data you already have, how your cameras are set up, what challenges you may face, and what results you can achieve.