Three waiting behind one at service
Four customers are visible: three behind the service position and one at the counter. The cashier is counted separately.
Opsyte for stores and shopping centres
Understand the store beyond the sale.
A busy store creates questions that transactions alone cannot answer. Where are people waiting? Which area is crowded? What happened in the stockroom? Opsyte helps store and centre teams turn selected camera views into structured events and operational context, from the entrance to the loading bay.
Preparing for launch · tailored walkthroughs available
VISION → CONTEXT → ACTIONRetail · checkout and waiting lineSynthetic LTX footage · manually annotated review frames
Paused00:00.0/ 00:06.0
Waiting behind serviceCustomer service positionCashier side
Annotated demonstration5 subjects in view
Analysis layers
Waiting behind serviceCustomer service positionCashier side
Clip analytics
KEY MOMENTSSelect a moment to pause and inspect
Person waitingVisible in this frame
Time visible in this clip0 sec
Waiting behind service
Three people behind the customer at the service position; the person being served is excluded.
Positions and counts were manually reviewed every 0.5 seconds from 0 to 6 seconds. Interpolated boxes are approximate visual annotations, not live model inference. Event markers identify a confirming sampled frame, not an exact crossing instant.
THE USE CASE01 / 04
Give a waiting queue a clear next step.
Observe
Watch a sustained checkout queue in the agreed area.
- Review area
- Checkout queue · Store
- Team to involve
- Store manager
Service attention at the right place
Queue and occupancy conditions give managers a practical reason to review coverage where the pressure is visible.
A richer view of how space is used
Observe footfall, dwell and activity in defined zones, then compare periods without identifying individual shoppers.
Evidence that keeps review focused
Find a relevant entry, object or out-of-hours event with its camera, location and time already attached.
THE USE-CASE LIBRARY
What would you like to see sooner?
Explore specific operational jobs—not just a list of AI features. Each example explains the condition, useful evidence and the team’s next step.
IndustryRetail & malls
10 examples
Retail & malls
Configured use-case exampleGive a growing checkout queue a next step
The operational need
Help store teams notice when checkout capacity deserves attention.
The camera view matters
Checkout queue
- 01
The condition to look for
The defined queue exceeds its configured threshold.
- 02
Evidence to review
Queue event, camera, time and a selected snapshot.
- 03
A useful next step
The store manager reviews the situation and adjusts checkout coverage.
Illustrative configuration, not a recorded customer outcome. Camera suitability, analysis scope and the response workflow are agreed during setup.
A WORKFLOW FOR YOUR OPERATION
From the first question to a useful review.
Follow the practical steps for this environment. Each one connects the technology to the people who will use the result.
- 01
Start with a store-level question
Choose a checkout queue, entrance flow or staff-area boundary that your operations team wants to understand.
- 02
Configure a clear observation
Define the area and condition for each selected view. A queue threshold and an access boundary need different configurations.
- 03
Put the event in the manager’s context
Show the store, camera and evidence and agree which events belong with operations, facilities or security.
- 04
Compare the patterns that repeat
Use the same defined objective to review periods and locations and identify where the team wants to change coverage or flow.
FROM CAMERA SIGNALS TO BUSINESS QUESTIONS
What could your cameras tell your business?
Turn a view into useful data. Explore model families for people, service, safety, movement and production. Configure the measurements around your operation—and connect them to the questions that matter.
Observe a defined signalA person, an object, a count or a configured condition.
Structure the informationConnect a model’s output to a time, camera and zone.
Investigate a patternCompare periods, inspect the source and plan a response.
MODEL → METRIC → DECISION
Illustrative datasetFootfall & arrivals
See when people arrive and how demand changes through the day.
A configured counting view supplies passage counts. The example compares two separate entrance views.
Illustrative data · change the location or period to recalculate every metric and report.
THE BUSINESS QUESTION
When should we prepare for the busiest arrival periods?
Passage counts in each observation window.
Observed arrivalspeople
Both example sites · 09:00–12:0015-minute intervals
Interval total. Adds interval values for the selected sites and period.
What this can help you understand
Higher bars show busier arrival periods; the total adds the selected windows and separate sites.
A passage count is not a count of unique customers or verified identities. Overlapping views need duplicate handling.FOLLOW THE NUMBER BACK TO ITS SOURCE
2 example observationsInside this interval 09:00–09:15
Entrance camera
Arrival boundary
- Observed arrivals
- 12 people
Entrance camera
Arrival boundary
- Observed arrivals
- 9 people
WHAT THE MODEL CAN COLLECT
Useful data. Clearly defined.
Observation window
The illustrative records use equal 15-minute periods. These are example records, not customer measurements.
Example09:00–09:15 · illustrativeCamera and monitored area
Every measure belongs to a selected camera, location and monitoring area.
ExampleSite A · selected camera and zoneObserved passages
People counted through the configured entrance or counting boundary during the period.
Example18 observed passagesCounting boundary
The defined entrance or line that gives the count its meaning.
ExampleMain entranceStart with the question you need answered.
Discuss these analyticsWe map the camera, model, collection fields and report to that decision—and validate what works in your environment.
INSIDE OPSYTE
See how the pieces work together.
From a connected camera to an event your team can understand. Explore the decisions and controls behind the experience.
Build on the cameras you have
Your existing cameras. A more useful view of the operation.
The first step is understanding what your cameras can show and how to access the selected feeds securely. Opsyte works with your IT or CCTV team to connect the views that support a real monitoring objective. Camera inventory, network arrangements and feed stability become part of a planned setup.
/Camera connectivityProduct illustrationSource
Analysis
Evidence
Review
A named camera. A clear location.Example: warehouse approach
- Source type
- RTSP / RTMP
- Connection review
- Check the feed and the field of view
Camera credentials belong in the secure onboarding flow—not this example.
A defined operational question01
Map the cameras that matter first
List the priority locations and what each selected view needs to reveal: a queue, boundary, bay or work area.
The example explains the setup. No live system is changed.
PUT IT TO WORK
What this means for your team.
The checkout queue grows
A configured threshold produces an event for the store manager, with the selected queue scene ready to review.
An entrance shows a recurring busy period
Available footfall observations help the team compare arrival patterns and plan service coverage.
Movement enters a staff-only area
A zone event gives the responsible manager context to check permitted access, rather than guessing intent from an image.
A PRACTICAL FIRST STEP
Start with one site. Make the workflow useful.
You bring the operational question. Together, we define the camera coverage, the analysis and what your team should do with the result.
0 / 4preparation items you marked ready
A local planning aid, not a readiness score or a submitted request.Your starting point
Give a growing checkout queue a next step
The next page opens an editable inquiry with your selected industry and scenario. No camera credentials or footage are needed here.
Discuss this pilotBEFORE YOU CONNECT
Clear answers. A better starting point.
Understand the practical details before you plan your first deployment.
01Can it help operations as well as loss prevention?
+
Yes. The platform can organize configured queue, footfall, occupancy and dwell observations alongside security-related events. Each objective has its own camera selection, useful evidence and responsible team.
02Does it identify shoppers or infer their intent?
+
The examples here concern visible counts, movement, zones and events. They do not identify shoppers, infer demographics or establish criminal intent. A flagged scene gives your team something to review in context.
03Can queues use a different threshold in each store?
+
The monitoring objective is configured around the selected camera and zone. Agree thresholds, hours and response ownership for each store rather than applying one unreviewed rule everywhere.
04How do we start without covering every camera?
+
Choose a small set of priority views and a useful first objective. We validate connectivity and tune that scope with your team, then plan expansion around the locations and questions that prove valuable.
Let’s find the moments that matter to your business.
Tell us what your team needs to see, understand or improve. We will help shape the right walkthrough and pilot scope.