Retail operations
Staff where and when customers are.
Capability
In-Store Video Analytics
Understand footfall, queues and dwell from the cameras you already own.
Footfall and capture rateVisitor-to-transaction conversionAverage queue wait and abandonmentDwell time by zone
The service at a glance
The problem, our work, your result
- Business problem
You know what sold, but not what happened inside the store.
Footfall, queues, dwell time and layout decisions are guesswork, and existing cameras record but never explain.
- What we do
We turn existing camera feeds into anonymous analytics: counts, flows, queue lengths and zone engagement, with privacy controls built in.
- What you receive
- Footfall and flow analytics
- Queue and wait-time alerts
- Zone and layout insight
- Privacy-first design
Who it is for
Built for the people who own the decision
Store planners
Layouts proven by movement data.
CFO and finance
More sales from the same space.
Risk, compliance and security
Anonymous analytics, no face storage.
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
Survey
Review camera coverage and goals.
- 02
Connect
Stream feeds to the analytics platform.
- 03
Calibrate
Tune counts and zones per store.
- 04
Act
Alert teams and inform layout decisions.
Engagement options
Start where you are
One store pilot
Prove value on existing cameras.
Region roll-out
Analytics across a store group.
Retail intelligence
Video plus shelf and sales data together.
Scope
What is in, what is not
Included
- Video analytics platform
- Camera integration
- Privacy controls
- Dashboards and alerts
Handled separately
- New camera supply
- In-store network cabling
Bilytica frameworks behind this service
Our own methods, not a generic playbook
TRUST
All Bilytica frameworksAI Assurance Framework
One control set that answers NIST, ISO 42001, the EU AI Act and the Saudi regulators at once.
See the frameworkDFFData Foundation Framework
The layers that turn scattered systems into data an AI system is allowed to use.
See the frameworkIn short
What this actually is
Computer vision on existing CCTV that counts visitors, measures queue length and wait time, maps dwell by zone and correlates all of it with sales. Processing runs at the edge and outputs counts and heat maps, not identities, the store learns how it performs without building a face database.
Key points
What matters before you buy
- 01
Conversion becomes measurable
Transactions over visitors, by hour and by zone, the number most retailers estimate rather than measure.
- 02
Staffing follows real traffic
Rosters built on measured hourly footfall instead of last year's assumption.
- 03
Anonymous by design
Detection and counting at the edge; frames are not exported and no biometric identity is created.
- 04
Works on the cameras you have
Most estates need lens angle and lighting fixes, not a hardware replacement programme.
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 camera inventory with usable angles over entrances, tills and key aisles.
- Store layout and zone definitions that match the planogram.
- POS transaction data at the same time granularity, or conversion cannot be computed.
- A posted privacy notice and a retention rule for footage.
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.
Counting, not recognising
No face matching, no cross-visit tracking of an individual and no demographic inference unless you have an explicit lawful basis and ask us for it in writing.
Fit
Systems it plugs into, and what it moves
Integrations
Existing CCTV / NVREdge GPUsPOS systemsWorkforce managementPower BI / Tableau / Qlik
KPIs it moves
- • Footfall and capture rate
- • Visitor-to-transaction conversion
- • Average queue wait and abandonment
- • Dwell time by zone
- • Sales per square metre
By industry