Retail
Shelves stocked, queues moving, customers converting. No manual audits, no new POS.
Retail AI from Allerin is shelf compliance, queue and staffing analytics and back-room inventory scanning that run on in-aisle and existing security cameras, measured by on-shelf availability, walk-offs and audit hours.
Who is this for?
Store operations and merchandising leads who answer for on-shelf availability and labor, and loss prevention teams who need evidence rather than hunches. It is for chains that already have an ERP or WMS and handheld devices on the floor. It does not require a new POS.
What problem does it solve?
An empty facing costs the sale and then costs the audit. Someone walks the aisle, finds the gap hours after it opened, and writes it on a form that reaches merchandising next week. Planogram drift is worse, because nobody is looking for it until a vendor complains.
Queues are the same story at the front of the store. The walk-off happened at 5:40 when the second lane should have opened at 5:30, and the only record is a dip in the hourly sales number that no one can explain. Back-room counts, meanwhile, eat the labor that should be on the floor.
How does it work?
ShelfSentra watches the aisle through in-aisle cameras, detects out-of-stocks and planogram drift, and pushes a task with a photo to the nearest handheld. Recovery is measured from the alert to the fixed shelf, which turns a vague compliance score into a number a store manager can act on.
Queuence counts entries and exits per lane, predicts queue length from the pattern, and fires lane-open alerts before the walk-off happens; abandonment and dwell are tracked over time so staffing can follow demand instead of the roster. Backroom Ranger scans the back room with the same handheld workflows and reconciles to the ERP, so the inventory number the store runs on is the inventory the store has.
Camera needs are stated honestly: queues usually work with the security cameras already installed, while shelf compliance needs in-aisle angles. We say which in the site survey, before anyone budgets hardware.
For loss prevention the same cameras produce evidence with a timestamp and a location, which is what an investigation needs and what a hunch never has.
Quick wins
- Shelf compliance & OSA (ShelfSentra)
- Queue & staffing analytics (Queuence)
- Back-room inventory scanning (Backroom Ranger)
Capabilities
- CV
- Analytics
Solutions for Retail
- YardLytix
Dock & Yard Analytics
- Queuence
Queue & staffing analytics
- ShelfSentra
Shelf observations, planogram checks and staff tasks
- StockVexel
Inventory scans, WMS reconciliation and exception review
What does it connect to?
- ERP and WMS adapters through REST and webhooks (SAP, Oracle, Dynamics)
- Handheld tasking with photo evidence
- Planogram ingestion through the API or a direct upload
- Existing security cameras for queues; in-aisle cameras for shelf compliance
How is it deployed, and how long does it take?
Edge inference gives the store real-time alerts; the cloud keeps the trend analysis and the category-level reporting. Planogram changes arrive through the API or an upload, and drift alerts follow automatically.
Not every category pays back equally. Fast-moving, high-margin categories show the OSA lift first; we start there and expand on the numbers, not on a chain-wide promise.
Proof of concept to pilot in 8 to 12 weeks with KPI gates. Quick-win payback of 3 to 5 months through OSA sales lift and reduced audit labor.
Related products and services
- SENTRA
In-aisle CV for shelf compliance and queue monitoring
- Data & Analytics Platform
Predictive staffing models and ERP integration