How accurate is product recognition on crowded shelves?

Accuracy depends far more on your product master and image conditions than on the model. We measure it per category on your own shelves during the first release and publish the number internally before scaling.

Do we need shelf robots?

Rarely, and never first. Fixed cameras on high-value aisles or scheduled staff photos cover most of the value at a fraction of the cost.

Manufacturing & Industrial · Shelf & planogram AI

Retail execution for brand owners

See out-of-stocks, share of shelf and planogram drift the day they happen.

Book the Data Foundation Assessment

On-shelf availabilityTime to replenish after a gap alertPlanogram compliance rateShare of shelf by category

The problem

What this fixes

You pay for shelf space and promotions you cannot verify. Field reps visit a fraction of stores and report from memory and a clipboard.

Image recognition over fixed cameras, shelf-scanning robots or staff phone photos that identifies products on the shelf, flags gaps, checks facings against the planogram and measures share of shelf against competitors. Alerts land in the store team's task app instead of a monthly audit report.

Manufacturing & Industrial

Share of shelf

Measured from rep photos across the estate, by category and by store cluster.

Manufacturing & Industrial

Promotion compliance

Verify that the display you funded actually exists, with dated evidence.

Manufacturing & Industrial

Out-of-stock alerts to the distributor

Gaps reported the same day rather than in a monthly audit.

Manufacturing & Industrial

Rep visit prioritisation

Route reps to the stores where compliance actually slipped.

In this sector

Where it earns its place

Delivery

How we build it

Foundation check

Before any build

We inspect the systems, the data behind them and the quality of the records this capability will depend on, then write down what has to be fixed before anything is built.

Narrow first release

First release

One workflow, one team, real traffic, measured against the baseline we captured before we started.

Controls and evaluation

Runs in parallel

Guardrails, approval gates, evaluation sets, logging and the evidence pack your auditor and your regulator will ask for.

Scale and hand over

After sign-off

Additional workflows, sites or languages, then runbooks and training so your team can operate it without us.

Data foundation

What has to be right first

A clean product master with images, barcodes and pack variants.
Current planograms per store cluster in a machine-readable form.
Stock-on-hand from the ERP to separate a shelf gap from a supply gap.
A task app the store team already uses.

Controls

Guardrails and KPIs

Every action is logged and replayable

Inputs, retrieved context, model version, output and the human decision are written to an immutable log, so an auditor can reconstruct any single interaction months later.

Data stays where your regulator requires

In-country, in-region or on-premise deployment, with the model portfolio right-sized to the workload instead of routing everything to a frontier endpoint abroad.

Accuracy is monitored, not assumed

A held-out evaluation set runs on every model or prompt change, with drift alerts and a documented rollback path when quality moves.

A human owns every consequential decision

Agents draft, route and execute reversible steps. Anything that moves money, changes a record of consequence or affects a person's rights stops at a named human approver with the reasoning attached.