The problem
Labour-intensive processes are usually managed with targets and end-of-day output. Where time is lost — waiting for material, unstaffed stations, uneven workloads — stays invisible.
How it works
- 1Stations are defined as zones on camera views.
- 2Presence and activity are detected per station over time.
- 3Active, idle and unstaffed intervals are aggregated per station and shift.
- 4Bottlenecks and imbalances are shown on dashboards for supervisors.
Existing IP cameras / NVR
RTSP / ONVIF streams
Edge device on-site
AI models + rules; video stays local
Events
Time, camera, zone, evidence frame
Dashboard, alerts, API
Notifications, relays, REST/webhook
What can be detected
- Station staffed / unstaffed
- Active versus idle time
- Task or cycle durations
- Bottlenecks between stations
Typical scenarios
- Sewing, ironing and packing stations
- Kitting and assembly cells
- Kitchen and service stations
- Warehouse pick and pack
Deployment options
On-premise / edge
Analysis runs on hardware inside your network. Video does not leave the site; only events and metrics are stored or shared.
Cloud
Suitable for distributed, low-camera-count sites where central management matters more than keeping video on-site.
Hybrid
Video is processed on-site, while events, dashboards and multi-site reporting are managed centrally.
Working with existing cameras
- Each station should be clearly visible; one camera can often cover several stations.
- Most IP cameras and NVRs that provide RTSP or ONVIF streams can be connected, regardless of brand.
- Before a pilot, sample footage from each candidate camera is reviewed for resolution, angle, distance and lighting.
- Where a view is not suitable, the recommendation is usually to reposition the camera or add one — not to replace the whole system.
Integration
- Dashboards and shift reports
- REST API for ERP/MES or HR-independent process reporting
Privacy and security
- No face recognition is used in safety and operations analytics; events describe what happened, not who.
- On-premise processing keeps video inside your network.
- Evidence clips, retention periods and user access are configurable and should follow your GDPR/KVKK policies.
- Workplace monitoring should be introduced with clear employee information and, where applicable, consultation with employee representatives.
Limitations
- Workplace monitoring has legal and employee-relations implications. Deployments should be designed with HR, works councils where applicable and data-protection advisers.
- Process-level reporting is recommended over individual-level monitoring. No face recognition is used.
How a pilot works
- 01
Discovery
We agree on the problem, the owner, the sites and what a successful outcome would look like.
- 02
Camera assessment
Sample footage from candidate cameras is reviewed for angle, resolution, distance and lighting.
- 03
Use-case definition
Events, zones, rules, alert recipients and success metrics are written down before deployment.
- 04
Pilot deployment
Edge hardware or cloud processing is set up and connected to the selected cameras.
- 05
Validation
Detections are checked against reviewed footage; true detections, false alarms and misses are counted per camera.
- 06
Report
Results, limitations and recommended changes are documented and shared with all stakeholders.
- 07
Rollout decision
Based on measured results, you decide whether and how to expand to more cameras, sites or use cases.
Frequently asked questions
Is this individual employee monitoring?
The recommended setup reports at station and process level. Individual-level use must be assessed with your legal and HR teams and local regulations such as GDPR or KVKK.
What do supervisors see?
Station-level dashboards: occupancy, active and idle time, and bottlenecks per shift.
Related solutions
Industries
Case studies
Start with the cameras you already have
Send us a few frames from your cameras and the problem you want to solve. We will tell you honestly what is feasible.