Retail operations
Gaps fixed before customers notice.
Capability
Shelf & Planogram Intelligence
See out-of-stocks, share of shelf and planogram drift the day they happen.
On-shelf availabilityTime to replenish after a gap alertPlanogram compliance rateShare of shelf by category
The service at a glance
The problem, our work, your result
- Business problem
Shelves are checked by eye, so gaps and wrong placements are found late.
Out-of-stocks and planogram drift cost sales every day, and manual audits cover a fraction of stores.
- What we do
We use cameras and image recognition to read shelves continuously: what is missing, what is misplaced, and whether displays match the agreed plan.
- What you receive
- Live shelf availability view
- Planogram compliance scores
- Restock and fix alerts
- Supplier compliance evidence
Who it is for
Built for the people who own the decision
Category managers
Proof that plans are executed.
Suppliers and brands
Shared, factual display data.
CFO and finance
Recovered sales from availability.
The goal: an AI-ready organisation
Where this service moves you
Data readyThis service
Connected, clean, governed and allowed for AI use.
KPIs readyThis service
Agreed measures, one definition each, a named owner.
Organisation ready
Roles, decision rights and skills to adopt AI safely.
=AI that reaches production and creates value
Approach
How we work with you
- 01
Map
Digitise planograms and priorities.
- 02
See
Train recognition on your products.
- 03
Alert
Route gaps to the right team.
- 04
Report
Score compliance per store and supplier.
Engagement options
Start where you are
Category pilot
One category in a few stores.
Store network
Shelf intelligence across stores.
Supplier programme
Shared compliance reporting.
Scope
What is in, what is not
Included
- Image recognition setup
- Planogram digitisation
- Alerts and dashboards
- Supplier reports
Handled separately
- Shelf hardware and cameras
- Merchandising staff
Bilytica frameworks behind this service
Our own methods, not a generic playbook
DFF
All Bilytica frameworksData Foundation Framework
The layers that turn scattered systems into data an AI system is allowed to use.
See the frameworkVALUEAI Value Framework
From use-case long list to booked benefit, with unit economics attached at every step.
See the frameworkIn short
What this actually is
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.
Key points
What matters before you buy
- 01
Out-of-stock is a revenue leak, not a reporting problem
Detection is only useful if it becomes a task with an owner and a deadline in the store.
- 02
Planogram compliance without the clipboard
Facings, sequence and price-label mismatches checked continuously rather than on an auditor's visit.
- 03
Supplier-grade evidence
Share of shelf and compliance images give category and trade-marketing teams something to negotiate with.
- 04
Phone photos are a valid starting point
A camera programme can wait; a good model on staff-captured images proves the value first.
Delivery
How we build it
Foundation check
Before any buildWe 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 releaseOne workflow, one team, real traffic, measured against the baseline we captured before we started.
Controls and evaluation
Runs in parallelGuardrails, approval gates, evaluation sets, logging and the evidence pack your auditor and your regulator will ask for.
Scale and hand over
After sign-offAdditional workflows, sites or languages, then runbooks and training so your team can operate it without us.
Data foundation
What has to be true in your data first
Great AI cannot hide bad data. These are the conditions this capability depends on, we fix them as part of delivery, not after go-live.
- 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 we put around it
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.
Fit
Systems it plugs into, and what it moves
Integrations
SAP / Oracle RetailBlue YonderRetail task management appsEdge cameras and shelf robotsPOS
KPIs it moves
- • On-shelf availability
- • Time to replenish after a gap alert
- • Planogram compliance rate
- • Share of shelf by category
- • Lost-sales estimate from gaps
By industry