AI checks every stand photo, one by one.
Nobody can look at tens of thousands of photos by hand. NN-FMCG finds non-compliant stands, missing competitor-area labels and recycled shots with scheduled scans, and your team still makes the final call.
NN-FMCG is merchandising software that uses AI to audit every stand and shelf photo your field team takes. A vision model compares each photo with the brand's approved stand images and returns compliant, non-compliant or no stand with a short reason, and it separately looks for the competitor-area label. Identical, cropped or re-saved photos are caught by their fingerprints. Scans run on a schedule, analysts correct a wrong result in one click, and an authorised person always makes the final decision.
Sound familiar?
Plenty of photos, no review
Tens of thousands of stand photos arrive every month. Nobody can check them all, and spot checks miss most of the stands with problems.
The competitor-area label slips through
Verifying a small label at the top of the shelf, photo by photo, takes hours. A missing label usually surfaces only during the audit.
Same photo, different visit
A cropped, re-saved or borrowed shot looks like a brand-new visit to the human eye.
Findings are scattered, action is late
Which store has a non-compliant stand, which one lacks the label? Without one list, feedback to the field arrives weeks late.
Step by step, on screen.
- all stand photos are scanned, not a sample
- Every photo
- compliant, non-compliant or no stand, with a reason
- 3 verdicts
- stand photos scanned by AI
- 28k+
- no photo is ever scanned twice
- 0 rescans
What matters most here
- Compliance verdict with a short reason, based on your reference stand images
- Competitor-area label detection on the full frame and a zoomed top shelf
- Identical and near-identical photo groups via SHA-256 and perceptual fingerprints
- Blank and black frames flagged separately
- One-click "this is wrong" feedback that builds up calibration data
- Scheduled scans with live progress, "Run now" and a findings summary email
- Per-store stand, label and duplicate-photo alerts
Questions you might have.
Which AI service is used, and where do the photos go?
Stand compliance and label checks run through an external vision model API (currently DeepSeek), and photos are sent to that service during a scan. Reused photo detection runs entirely on our own servers with classic image processing.
What happens when the AI gets it wrong?
The AI only flags; an authorised person always makes the final call. Analysts correct a wrong result in one click, and every correction is stored as calibration data for measuring the model’s verdicts.
Does it check against our own stand standards?
Yes. The model compares every photo with the stand images you have approved. Blurry or screen-photographed images are still assessed, and every verdict comes with a short reason.
How does the photo check work if the store has no internet?
The photo is kept in an offline draft and uploaded when the connection returns. The AI scan runs on the server on a schedule, so the rep never waits in the field; the photo is checked in the next scan.
How much does it cost?
Pricing depends on the size of your field team and the modules you use. Book a demo and we will show it working on your own stand photos and prepare a tailored quote.
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See it live on your process.
We will run a 30-minute live demo on a scenario close to your own data.
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