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AI shelf analysis for FMCG

See the stock. Track the trend.

Your reps are already in the store. ShelfPro turns the photo they take into on-shelf availability, share of shelf and planogram compliance in seconds — and tracks every number, visit after visit.

Works with no signalRuns on the phones reps already carry
Share of shelf41% · this fixture
Planogram2 facings off plan
No signalCapture queued — uploads later
A field rep holding a phone up to a supermarket shelf. On the screen, every pack on the shelf is outlined in green and one empty space is marked in orange.
VISIT IN PROGRESSAisle 4 · shelf 2 of 3
Drag to reveal

See ShelfPro in action

Left of the handle is the photo the rep took. Right of it is what ShelfPro reads from it: every pack, every gap, every price tag.

AQUA 0.96AQUA 0.90OUT OF STOCKCITRUS 0.93VERDE 0.89VERDE 0.99GOLD 0.88ROSSO 0.96ROSSO 0.88OUT OF STOCKAQUA 0.94GOLD 0.95CITRUS 0.88VIOLA 0.91GOLD 0.96GOLD 0.96OUT OF STOCKAQUA 0.94ROSSO 0.95VIOLA 0.88VERDE 0.91€0.79€1.29€1.29€1.49€2.49€1.99€0.99€0.79€2.49€1.29€0.99€1.992.49€1.49€0.79€1.99€0.99€1.49
FACINGS: 18 MATCHED
← SHELF PHOTOWHAT SHELFPRO SEES →
  • Soft drinks9
  • Juice & tea5
  • Water4
  • Out of stock3
  • Price tags17

What this one photo turns into

On-shelf availability
86%18 of 21 facings stocked, 3 flagged out of stock.
Share of shelf · Aqua
19%4 of 21 facings, gaps included in the shelf.
Price tags read
17One on promotion: Gold Reserve 2L at €1.99, down from €2.49.
SKUs recognized
6Every facing matched to a product in the catalog, with a score.

The shelf, its boxes, scores and price readings are an illustrative fixture in the API's own data shape, drawn rather than photographed — no customer imagery is cleared for public use.

$1.2Tlost globally each year to out-of-stocksIHL Group, 2025
6.5%of global retail sales lost to stock distortionIHL Group, 2025
~8%average out-of-stock rate on shelfNielsenIQ
46%of shoppers don't substitute — the sale is simply lostNielsenIQ

Category-wide figures from published research, not ShelfPro claims.

The problem

You cannot fix what you cannot see

Manual shelf audits are slow, inconsistent and generous. By the time the report reaches head office, the promotion is over and the sale is gone.

Manual audits are slow and generous

A rep under time pressure checks “compliant” and moves on. Counts vary by person and by day, and nobody can check them afterwards.

Evidence:a checkbox on a form

The data arrives after the promotion ends

A gap found in next month's report is a sale already lost. A weekly spreadsheet cannot rescue this week's display.

Seen:in next month's deck

A photo in a chat group is not a dataset

Close-ups capture fragments. Share of shelf and competitor space need the whole fixture, in a form someone can actually count.

Coverage:fragments of the aisle
The process

Three steps. No change to how your team already works

Capture

The rep photographs the shelf on the phone they already carry. Wide fixtures are captured in a single pan, with on-screen guidance for framing. It works with no signal — the store basement is not a problem.

Recognize

Our AI identifies every facing and every gap against your product catalog, then turns them into the KPIs your commercial team already reports on.

Act

Results are ready before the rep leaves the store. Missing stock gets flagged to the store manager on the spot, not in a report next month.

What you get

All your shelf KPIs from a single photo

Everything below comes out of the same capture. No extra visit, no separate audit program, no clipboard.

1. On-shelf availability

Which of your SKUs are missing, in which stores, right now — the single number most directly tied to lost sales.

2. Out-of-stock alerts

Gaps are flagged while the rep is still in the aisle, so the store manager can fix them during that visit rather than after the next one.

3. Share of shelf

Your facings against the category and against named competitors. The evidence you take into a retailer negotiation.

4. Planogram compliance

Whether the shelf actually looks like what you paid the retailer for — position, block, eye level, facing count.

5. Promotion execution

Did the display go up, in the right stores, on the right day? Measured while the promotion is still running.

6. Competitor activity

New SKUs, new packs and space grabs, spotted from the same photos your reps already take.

7. Field team performance

Coverage, visit quality and consistency by rep and by region — coaching data, not surveillance.

8. An evidence trail

Every number traces back to a timestamped photo from a known store and visit. Nobody argues about whether the display was really up.

9. Your reporting stack

Results flow into your BI or field tool through a documented API — or your team reads them in ShelfPro's own dashboards.

Offline capture

The stockroom has no reception — but the visit still gets done

Basements and back rooms kill the signal. ShelfPro captures, checks and queues every shelf on the device, then uploads in the background once the signal returns. The rep never waits for a spinner and never loses a visit.

  • Capture and check framing with no connection at all
  • Uploads resume on their own when the signal comes back
  • Nothing is retaken, nothing is lost — every capture is kept until it is safely through
No signalstore basement

Store 104 · aisle 4

Shelf 1 of 3 · 10:42

Analyzed
Analyzed

2 captures queued

Uploads when signal returns

on device
on device
VISIT CONTINUES OFFLINE Ready
ONE PAN · 4 FRAMES1 SCENE
Share of shelf measured across:the whole fixture
Whole-fixture capture

A long shelf is one scene, not a dozen overlapping photos

Reps pan along the fixture and ShelfPro joins the frames into one continuous shelf before anything is counted. Facings at the seams are counted once, and share of shelf is measured across the whole fixture rather than guessed from close-ups.

  • No double counting at frame boundaries
  • Share of shelf across the full fixture, not a sample of it
  • The whole fixture on one review screen, gaps and all
Why teams switch

What changes in the first quarter

Reps spend their visit selling

Counting facings by hand is the least valuable thing a rep does in a store. A photo takes seconds and is more accurate than the count it replaces.

Data you can defend

Every number traces back to a timestamped photograph. Nobody argues about whether the display was really up.

Problems surface same-day

Availability gaps reach the person who can fix them while the shopper is still in the store, not in a monthly deck.

It gets better as you use it

When your team corrects something the model got wrong, that correction trains the next version on your catalog.

Comparison

Against the clipboard you're using today

Time per shelf audit

20–30 minutes

Under a minute

Consistency between reps

Varies by person and by day

Identical every time

When you see results

Next weekly or monthly report

Before the rep leaves

Evidence behind a number

A checkbox on a form

A timestamped photograph

Competitor visibility

Anecdotal

Measured from the same photo

Cost of covering more stores

Linear — more reps

Marginal

Business case

What would ShelfPro be worth to you?

Move the sliders to your own numbers. The math is shown, the assumptions are yours to change, and nothing you type is sent to us.

Your numbers

Change anything here. Nothing is sent to us.

400

Outlets in your visit plan

2

Your call cycle

€60,000,000

Sell-out value

8%

Category average is ~8% (NielsenIQ)

Estimated annual impact

€1,200,000

sales recovered from fewer empty shelves

€4,800,000 is currently at risk; this assumes you close 25% of it.

€66,880

field time released

3,040 hours a year — 19 minutes saved across 9,600 visits.

Reported separately on purpose: recovered sales and saved cost aren't the same line on your P&L. Apply your own gross margin to the first figure before comparing the two.

Get this modeled on your data
Questions

Before you book a call

Do we need new phones or hardware?
No. It runs on the Android and iOS phones your reps already carry. No scanners, no rigs, no shelf cameras.
What if there's no signal in the store?
Captures are stored on the device and upload themselves when signal returns. Reps never wait and never lose a visit.
How long until it recognizes our products?
A pilot trains a model on your catalog and typically runs for eight weeks, including the first rounds of field capture.
What happens when it gets something wrong?
Your team corrects it in the review screen in a click, and that correction becomes training data — so the same mistake gets rarer.
Can it work with our existing field app?
Yes. ShelfPro can run as your capture app or sit behind the one you already use, feeding results into your reporting stack.
Where is our data held?
In the EU. We're based in Zagreb and operate on EU infrastructure.
Next step

See it on your own shelves

Send us photos from a handful of your stores. We'll come back with the analysis and what it says about your availability — before you commit to anything.

Technical evaluation? Documentation and API reference are here.