AI video analytics for retail chains

AI camera data showing what happens at the checkout

LiveFlow analyses video from cameras above the checkout area and turns it into clear data on queues, waiting time, customer service and checkout occupancy — all in one dashboard for HQ and operations teams.

No manual video reviewLive and historical dataReady for a POC pilot
Camera view · checkout area
REC
ENTRANCE
Checkout 2
busy
Queue
5
Time
3:20
Trend
Queue · 12 min
In queue
7ppl
Avg. time
2:15min
Open lanes
2/3
Cust./h
148+12%
Click a checkout to see its live data

Chains know queues are a problem — but often not when, where and why they form.

In many stores, decisions about checkout staffing, service standards and work organisation still rely on observation, one-off reports or manual review of recordings. LiveFlow structures this process and delivers measurable data from AI cameras.

No data on real waiting time

Short queues don't always mean fast service. LiveFlow shows how long customers actually wait.

Hard to compare stores

HQ needs shared metrics to evaluate locations against the same criteria.

Manual video review is inefficient

CCTV shows the picture but doesn't automatically produce metrics, trends and reports.

No fast response to overloads

Data on checkout occupancy and queue length helps identify peak moments faster.

How it works LiveFlow?

From camera image to data for HQ.

1

AI camera above the checkout

CASHIER · 98%PERSON · 94%PERSON · 91%LIVE AI

The camera observes a selected area at the checkout and feeds video to AI analysis.

2

Zones and measurement logic

CHECKOUT ZONEQUEUEENTRANCE ZONE

We define rectangular measurement zones: checkout area, queue and entrance zone.

3

AI analysis

VIDEOAI MODEL12PERSON DETECTION4QUEUE LENGTH2:15WAITING TIME3/4ZONE ACTIVITY

The system detects people, counts the queue, measures waiting time and analyses zone activity.

4

LiveFlow dashboard

Interactive view — switch the range, hover over bars and rows.

Queue analytics
Avg. waiting time
01:18
min
+6s vs yesterday
Avg. service time
30
sec
stable
People in queue
6
people
daily avg.
Checkout occupancy
79
%
peak 11-13
Hourly queue and throughput distribution
0608101214161820
First / last customer
06:34 / 21:59
From live data
Longest break
10 min
11:11–11:21
Busiest hour
11:00–12:00
131 customers
Today's queue peak
8 people
Most in queue
Day comparison
25.06.2026
Thursday
24.06.2026
Wednesday
Served customers
415
987
-572
Queue peak
8 ppl
8 ppl
no change
15-min peak
51 ppl
53 ppl
-2
Avg. waiting time
01:36
01:19
+17s
Occupancy peak
100%
91%
+9pp
Time slots and cashier performance
CSVXLSX
Slot
Served
Avg. wait
Avg. service
Occupancy
06:00–07:00
21
00:42
00:23
32%
07:00–08:00
56
00:59
00:27
41%
08:00–09:00
65
01:21
00:36
65%
09:00–10:00
79
01:18
00:32
70%
10:00–11:00
105
01:31
00:34
98%
11:00–12:00
131
01:09
00:22
79%
12:00–13:00
117
01:22
00:21
69%
No manual video review
Live and historical
Ready for a POC
8 metrics · updated every 15s

What data does LiveFlow?

The system focuses on AI camera data. Click a metric to see details, or filter by area.

Friday 19.06.2026
Traffic peak 11:00–12:00 · 131 customers
Avg. waiting time
01:18
min
+5s vs yesterday
Avg. service time
30
sec
stable
People in queue
6
ppl
live
Checkout occupancy
79
%
peak 11-13
Served customers
415
today
Hourly peak
11–13
traffic
Live and historical data in one view

What does a retail chain?

interactive view · 1/4
live · benefit 1
-38%
avg. waiting time
Without LiveFlow6:20
With LiveFlow3:55
updates every 2sauto-cycle

For which venues?

LiveFlow works wherever queue length, waiting time and service efficiency shape customer experience and operational effectiveness.

Retail
Convenience
Supermarkets
QSR
Petrol
Service points

Start with a POC in a single location

LiveFlow can be tested on a single checkout zone, without building a large project on day one.

POC process · step 1/4
STEP 1 / 4LocationDay 101Location02Setup03Data04Report
01

Location selection

Together we pick a store and a checkout area to analyse.

StoreCheckout zone
Day 1
auto-cycle 3.5s
Privacy and security

Data analytics, not manual video watching

LiveFlow focuses on turning AI camera video into operational data. The system's goal is to analyse queues, waiting time and zone activity — not to manually observe customers.

Data presented as metrics and reports.
Works on defined measurement zones.
POC scope agreed individually with the client.

Data processing scope and security requirements are agreed with the client during deployment.

See what really happens at your checkouts in your chain

Book a LiveFlow POC and see how AI camera data can help analyse queues, waiting time and checkout area work.

Let's talk about POC

Leave your details — we'll tailor the pilot scope to your chain, store formats and peak hours.

E-mail: kontakt@liveflow.pl
powered bySignal Group