Shelf Photo Stock Counter

For

Operations managers at multi-site cafe groups and small supermarket chains

Solves

Stock counts rely on clipboards and guesswork, causing waste and stockouts.

Delivers

Approved supplier order and waste log with photo evidence

Built in

about 2 weeks of creation time, MVP in 2 days

Investment

$5,500 for the MVP, $17,000 for the full product

Run it

Inside your business, or as part of your offer to clients

For operations managers at multi-site cafe groups and small supermarket chains, turn shelf photos, POS sales data and supplier catalogues into a draft supplier order with a waste flag list for approval.

1. Recognise SKUs from label text, barcode, can shape or shelf position.
2. Count visible units per SKU from a single photo.
3. Compare counts against weekday pars and POS depletion rates.
4. Generate a draft supplier order with quantities and suggested lines.
5. Flag discrepancies where stock fell faster than sales explain.
6. Attach the source photo to every flagged line for review.

What goes in, what comes out

The workflow

1. In
   Start with

   Shelf photos, POS sales data, supplier catalogue, weekday pars

2. 1

   Photograph shelves

3. 2

   Confirm zone coverage

4. 3

   Review recognition confidence

5. 4

   Adjust counts where needed

6. 5

   Review draft order

7. 6

   Approve flagged waste lines

8. 7

   Send the order to the supplier

9. Out
   Finish with

   Approved supplier order and waste log with photo evidence

AI does the heavy lifting, people stay in charge

Use vision models to detect and count products, and language models to match recognised items to catalogue entries. Keep counts and par values in structured fields. Validate totals and order quantities through deterministic checks. A manager reviews flagged discrepancies and the final order before it sends.

What your team sees

Key screens: Capture queue, stock tally, order draft, waste review. Use a photo grid for capture status, a SKU-level count table with confidence indicators, a draft order with line-by-line adjustments, and a waste review panel showing flagged lines with the original photo. Let users approve or edit each line. Display par, usage and discrepancy for every SKU. In this product, the first view is capture queue, followed by stock tally, order draft and waste review.

Accounts and administration

Site ownership, photo timestamps, user roles, order versions, approval states, audit trail of count adjustments, and a record of which manager approved each order.

Integrations and data access

POS systems for sales data, supplier catalogue feeds, and email or API for order submission. Start with a CSV export from the POS and a manual supplier order upload.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   2 days

   One buyer segment, one recurring use case; first modules: photo capture and SKU recognition, plus draft order generation. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   3 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   6 days

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will operations managers at multi-site cafe groups and small supermarket chains use it to solve "stock counts rely on clipboards and guesswork, causing waste and stockouts"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Run a four-week paid pilot at one site.
4. Measure, then decide. Track two measurable outcomes: count time reduced by 50% per site per week and and waste value reduced by 15% within the first quarter. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. One buyer: operations manager at a three-site cafe group. One use case: dry store counts. First two modules: photo capture and SKU recognition, plus draft order generation. Manual review of all flagged lines and order totals.

After the MVP. After the paid pilot, automate POS integration for depletion rates, add cold room and prep bench zones, and introduce automatic retraining when packaging changes are detected.

What the build depends on. A structured supplier catalogue, reliable phone camera capture, and a stable set of SKU pack formats for the first forty items.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test pricing at USD 300 per site per month, with a setup fee of USD 1,500 for catalogue mapping and recognition training.

Message to test

Turn a shelf photo into next week's order and waste log before your coffee cools.

Where to find buyers

Industry trade shows, food service distributor partnerships, and targeted LinkedIn outreach to district managers in cafe and supermarket groups.

Lead magnet

A free shelf photo analysis for one zone, showing the draft order and waste flags for a single day.

The first 30 days

1. Week 1: Build photo capture and upload flow.
2. Week 2: Train recognition on the top forty SKUs from the pilot site.
3. Week 3: Connect par and POS CSV import, generate draft orders.
4. Week 4: Run a live trial with two sites and collect accuracy feedback.

Paid pilot

Run a four-week paid pilot at one site. Baseline: manual count time and weekly waste value. Decision: continue if the draft order is accepted without edits for 80% of lines and waste flags catch at least one real issue per week.

Success metrics

Two measurable outcomes: count time reduced by 50% per site per week, and waste value reduced by 15% within the first quarter.

Retention and expansion

The system learns each site's pars and packaging quirks, so it becomes more accurate over time. Monthly retraining and new SKU onboarding keep it sticky, and the waste log builds a trend report that managers rely on.

Why clients would pick it

The system learns each site's shelf layout, par behaviour and packaging quirks, so switching costs rise as the recognition model is tuned to the client's catalogue and daily rhythms.

Alternatives and positioning

Clipboards, spreadsheets and manual reordering. This differs by turning an existing photo habit into a structured count and order draft with waste flags, rather than adding another data entry step.

Main delivery costs

Cloud vision API usage, model retraining compute, integration engineering for POS and supplier feeds, and support time for packaging changes.

Limit photo capture to authorised staff with timestamps and geotags. Require manager approval before any order sends. Do not auto-order without human sign-off. Keep a full audit trail of count adjustments and approvals. The system must not delete or override par values without an explicit manager action.