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Retail Analytics Software
for stores at scale.

SwiftVision AI turns your existing store cameras into footfall counts, heatmaps, queue alerts, dwell time and conversion data, across one site or a thousand, in real time.

Retail analytics dashboard with footfall counts, queue wait time, multi-store comparison and hourly traffic heatmaps
1000s
Stores Supported
Real-Time
Queue Signals
GDPR
Privacy First
+95%
Avg Conversion Lift

Retail Video Analytics Features
for every store.

Retail video analytics uses computer vision on in-store camera footage to count visitors, map where they go and measure queues. SwiftVision AI turns that into numbers store and operations teams can act on.

Customer heatmaps and dwell time analytics
In-store customer journey and path tracking
Footfall counting and conversion benchmarking
Privacy-first on-device inference at the edge
Real-time queue length and wait time monitoring
Staff coverage and service analytics
Multi-store comparisons by region or store group
POS integration for conversion attribution

Store Analytics Dashboard
for daily retail operations.

Store and regional managers see a live store heatmap, footfall, queue wait, dwell time and conversion KPIs in one place, with hourly traffic and multi-store benchmarking.

Store analytics dashboard with a live in-store heatmap, footfall and queue wait KPIs, dwell time and hourly traffic chart

How retail analytics works,
in four simple steps.

From the cameras already in your stores to live footfall, queue and shelf data, processed on-device at the edge.

1
Ceiling CCTV camera pointed at a grocery store entrance, with AI boxes around shoppers walking in

Use the cameras you already have

Connect the CCTV cameras already at your entrances, tills and aisles. No extra sensors or people counters to install.

2
CCTV view of a grocery checkout with AI bounding boxes around shoppers and the queue zone highlighted

Edge AI reads every frame

On-device AI detects people, queues and empty shelf space next to the cameras, so video is not streamed to the cloud.

3
CCTV view of a grocery aisle with an empty shelf gap outlined in red and a footfall heatmap on the floor

Store KPIs are calculated

Footfall, dwell time, queue length, wait time and conversion (with POS data) are calculated for every store in real time.

4
Store manager checking a queue alert and live AI camera detections on a tablet

Act on alerts and compare stores

Managers get alerts for long queues or empty shelves, and regional teams compare stores side by side in one dashboard.

Retail AI Use Cases
for stores, grocers and chains.

Use the cameras already in your stores to see traffic, queues and shelves, with video processed on-device at the edge.

Specialty Retail

Real-time footfall and queue signals show managers when to move staff to the floor or the till, so queues stay short.

Grocery and Big-Box

Aisle-level traffic and dwell time, plus out-of-stock detection on shelves, across large store formats.

Multi-Site Operations

The same footfall, conversion and queue KPIs in every store, so regional teams can compare sites like for like.

Retail video analytics demos
with live zone tracking.

Footfall
0:30

Store Entrance Footfall Counter (In & Out)

Kitchen
0:30

Restaurant Kitchen & Chef Staff Monitoring

Store Zones
0:07

Staff & Customer Activity Monitoring by Zone

View all retail AI demos →
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Retail analytics case studies
with results you can measure.

+95% conversion lift

Specialty retail chain lifted conversion by 95%

Problem • Queues built up at peak hours while staff were spread unevenly across the floor.

Result • Live queue length alerts now tell managers when to open another till or move staff to the front.

4× faster response

Grocery chain responds to empty shelves 4× faster

Problem • Empty shelves were only spotted on manual walk-rounds, often hours later.

Result • AI shelf monitoring on existing cameras flags out-of-stock gaps and alerts staff within minutes.

220 stores live

220-store retail analytics rollout in 90 days

Problem • Each of the 220 stores measured footfall and conversion differently.

Result • One set of footfall, dwell time and conversion KPIs now covers the whole chain.

Retail video analytics,
your questions answered.

The questions store and operations teams ask most.

What is retail video analytics?

Retail video analytics uses computer vision on in-store camera footage to count visitors, see where they go, measure queues and check shelves. SwiftVision AI turns that footage into footfall, dwell time, queue and conversion data that store and operations teams can act on.

Does it work with our existing CCTV cameras?

Yes. SwiftVision AI runs on the cameras you already have in store, so you do not need to install a separate sensor network. Camera positions for each use case are reviewed with you before rollout.

Is store video sent to the cloud?

No. Video is processed on-device at the edge, close to the cameras, instead of being streamed to the cloud for analysis.

How does AI footfall counting work?

The system detects each person at the entrance, follows them with a tracking ID and counts them as they cross the door line, so you see people coming in and going out in real time.

Can it detect queues and out-of-stock shelves?

Yes. It measures queue length and wait time at the tills in real time, and shelf monitoring flags out-of-stock gaps so staff can refill them quickly.

Can it connect to our POS system?

Yes. POS integration links footfall to transactions, so you can see conversion rate by store and by hour, not just visitor numbers.

Does it work across multiple stores?

Yes. Every store reports the same footfall, dwell time, queue and conversion KPIs, so the dashboard can compare stores by region or store group.

How much does retail video analytics cost?

The cost depends on how many stores and cameras you have and which modules you need, such as footfall, queues, shelf monitoring or POS integration. If you want a number for your stores, book a demo and we will talk through scope and pricing.

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