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
Find the useful moment
Focus a camera on a clear operational question.
Give it context
Review the event, its source and the supporting evidence.
Keep people in control
Help the right team review and respond to what matters.
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.
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.
FROM ATTENTION TO A DECISION
An alert is the beginning. Context is what helps.
Bring the source, visible condition and supporting evidence into one conversation with the team responsible. Explore a local review example.
A boundary becomes a useful alert.
Person entered the configured zone
Evidence to consider
Camera, event time and a selected scene snapshot
- Location context
- Exclusion zone · Site works
- Responsible team
- Site supervisor
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?
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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?
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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?
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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?
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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.