Planogram compliance software checks whether the products on a shelf match the layout the brand or retailer intended — the planogram — and flags every deviation. Done with AI image recognition, that check goes from a slow manual audit to a continuous, objective measurement.
Why planogram compliance matters
Planograms encode hard-won decisions about which products sit where, at what facing count, at eye level or below. When compliance slips, best-selling items lose visibility, promotions underperform, and trade-marketing money is wasted. The trouble is that manual audits are infrequent, subjective, and expensive.
How AI shelf monitoring checks compliance
- Recognise — a vision model identifies each product and its position from a shelf image.
- Map — detected items are matched to the planogram, facing by facing.
- Score — the system reports a compliance rate, share of shelf, and a list of specific fixes.
- Act — deviations become tasks routed to the right person in store.
The metrics that matter
Compliance rate, share of shelf, out-of-stocks (a close cousin — see our guide to out-of-stock detection with computer vision), and time-to-correction. Together they turn merchandising from opinion into measurement.
Manual audits versus AI
A field team can audit a store in tens of minutes and visit only occasionally. An image-recognition system measures every captured shelf, every time, with the same yardstick — and it improves as it sees more of your specific products.
How to roll it out
Pick one category and a small store set, agree the target metrics, and run a short pilot. We do this with Shelfzar in a 14-day proof-of-concept on your own infrastructure, then scale once the numbers are proven. To help shape the roadmap, see the founding cohort.