Methodology
Review of the measurement methodology, including real-world audience measurement use cases.
Data Reliability
Analytics You Can Trust at Scale
Monitoring hundreds of cameras continuously — so your data stays accurate even when the real world doesn’t cooperate.
5,000+cameras monitored
<5 minincident detection
<1 hrrecovery
98%+average data completeness at POI activation
6automated quality stages
95%+agreement with manual ground truth
Why reliability matters
Reliable AI starts with reliable visual data.
Camera movement, zoom changes, partial obstructions, unstable streams and degraded network conditions can affect analytics. Digeiz continuously monitors both the video infrastructure and the generated data.
01Video Infrastructure
02AI Processing
03Data Monitoring
04Reliable Analytics
01
Video infrastructure monitoring
The platform monitors image stability, camera configuration, obstruction, stream availability, latency, resolution consistency and system performance.
Cameras · Servers · Network · Video streams02
Data monitoring and quality assessment
Automated tools monitor KPIs, identify missing data and compare current metrics against historical patterns to surface anomalies.
KPI monitoring · Abnormal patterns · Missing data · Completeness03
Data reconstruction
When gaps occur due to hardware, network or power incidents, statistical models use historical traffic patterns and related datasets to reconstruct information transparently.
Gap detection · Dependency mapping · Reconstruction · Incident logs04
Data quality validation
The end-to-end quality process verifies output accuracy, dataset completeness and access to historical data across six automated stages.
Camera intake · Monitoring · Detection · Ticketing · Reconstruction · Quality checksIndependent verification · CESP 2024
Accuracy, stress-tested.
A CESP audit evaluated Digeiz AI over an 18-month process, examining how methodology, algorithms and real-world field tests support the accuracy of its metrics.
18 monthsaudit process
77 pagesindependent report
3 stagesmethodology, code, field tests
Algorithms
Code review of the executables associated with data computation and metric generation.
Field tests
Accuracy testing of metrics in real-life conditions, including in-mall environments.
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