[ 02 ] Retail
AI vision for retail, malls and restaurants
Turn existing cameras in malls, stores and restaurants into one system for visitor analytics, operations and security, processed on-site with privacy in mind.
Who it’s for · Mall and property managers, retail operations and marketing heads, loss-prevention and security managers, quick-service restaurant operators, and the telecom and system-integrator partners who serve them.
[ 01 ]Why Nabrio in retail
Built for how your sites actually work.
- 01
One system, three jobs
Analytics, operations and security come from the same cameras and the same platform, instead of a separate vendor for each.
- 02
Counts visitors correctly
Person re-identification recognises the same visitor across entrances and devices, so nobody is counted twice.
- 03
Processed on-site
Video stays on your premises. Demographics are anonymous estimates, and face recognition is used only for watch-lists you define.
- 04
Shares data with your systems
Results flow to dashboards and to other systems over MQTT or HTTP.
[ 02 ]Customer stories
Already running.
[ 03 ]Featured use cases
What retail teams build on Nara.
cameranara seesPeople counting
Simple door counters double-count people who return or use several entrances.
Heatmaps and dwell time
Operators can count visitors, but cannot see where they go, how long they stay or what catches their attention.
cameranara seesVisitor demographics
Operators know how many people visit, but not who they are, which makes tenant, marketing and event planning guesswork.
cameranara seesStaff presence monitoring
Staffing must match demand and standards, but managers lack a real-time view across stores.
Queue monitoring
Long queues lose customers, and in factories work-in-progress builds up unseen until it stops the line.
Intrusion detection
Sites need to know straight away when people or vehicles enter closed areas after hours, without a guard watching every screen.
[ 04 ]Every use case
The full list.
Everything in this sector that Nara can do, grouped by area. Use cases with their own page link through.
Visitor analytics
- People counting in and out, with re-identificationSimple door counters double-count people who return or use several entrances.
- Zone traffic and heatmapsCameras → tracking → visits per zone, paths, heatmaps → which areas, stores, and displays attract or lose traffic.
- Demographic analysis (age, gender)Face or person crop → anonymous age and gender estimates → visitor profile by zone and time, to plan tenant mix and campaigns.
- Engagement analysis: dwell time and attentionTracking gives the time spent in each zone; head pose and gaze show where people look (displays, windows, screens) → engagement per display or campaign.
- Expression and sentimentFacial expression model (happy, sad, angry, surprised…) → mood trends by zone or event.
- Shopping-bag analyticsDetect bags carried by visitors leaving → an indicator of conversion and purchases by entrance and time.
- Pet and pet-cart countingDetect pets and pet carts at entrances → counts for pet-friendly mall programmes.
- Event analyticsThe counting, zone traffic, demographics, and engagement above, applied to event spaces → attendance and audience profile per event, for organisers and sponsors.
Store operations
- Staff presence and role monitoringStaffing must match demand and standards, but managers lack a real-time view across stores.
- Queue monitoringCamera on the checkout or counter → count people in the queue zone and their waiting time → call more staff when the queue passes a threshold.
- Shelf and product availabilityCamera on shelves → empty-space detection or a "how full is this shelf?" score → restock alerts; planogram checks with a custom model.
Security and loss prevention
- Loss prevention: asset removalWatch high-value or fixed assets; alert when they are moved or removed
- Watch-list person detectionFace recognition against an operator-defined watch-list, with anti-spoofing
- Intrusion detectionPeople in closed or restricted zones, after hours
- Height risk and climbing on railingsPose + zones at balconies and atriums
- Fire and smokeCamera-based early warning
- Weapons (firearms, knives)Detect weapons and track the people involved across cameras
- FightsViolence and action recognition; track the people involved
- Falls and lying downPose action and fall detection; urgent alert
- Abandoned bagsStationary unattended object in a zone longer than a set time
Vehicles and parking
- Car traffic analyticsCount vehicles in and out by hour; peak times
- Car park occupancy and dwell timeVehicles per bay or zone; how long each vehicle stays
- Licence plate recognitionPlate detection + OCR; VIP or tenant recognition; parking duration
- Car brand and model recognitionClassify make and model, for customer profiling
[ 05 ]Integrations
Connects to your systems.
Mall and store CCTV over RTSP, dashboards for operations and marketing teams, and partner or head-office systems over MQTT or HTTP.
All integrationsFAQ
Retail: questions
Does it identify shoppers?
No. Age, gender and expression are anonymous estimates. Person re-identification works from overall appearance, not faces. Face recognition is used only for watch-lists the operator defines.
Does it work with our existing CCTV?
Usually, yes. Nara reads standard IP cameras over RTSP. Our shopping mall deployment runs on 120+ existing mall cameras.
Where is the video processed?
On-site, on your own hardware. Only the results you choose are shared with other systems.
Plan a proof of concept for your retail site.
Download Nara and build your first flow in minutes, or tell us what you need to see and do, and we’ll show you how it runs on your own hardware.