Strategy, technology, and operational insights from the team building AI video intelligence for the real world.
Most organisations calculate security spend by what they pay, not by what they lose. When we modelled the total cost of reactive CCTV deployments versus proactive AI monitoring across 40 customers, the gap was staggering.
Recording footage to review after an incident is table stakes. This post walks through what a genuinely proactive AI surveillance stack looks like: from edge inference to alert routing to evidence management.
A framework and worked example for calculating the financial return on an AI video intelligence deployment, including the three cost categories most RFPs forget to include.
A plain-English explanation of object detection, pose estimation, optical flow, and OCR, and when each technique applies to real operational problems.
A practical guide to deploying AI video intelligence in GDPR-governed environments, covering data minimisation, purpose limitation, DPIA requirements, and on-premise vs. cloud considerations.
We pulled data from 18 warehouse deployments comparing compliance rates under manual spot-checks versus continuous AI monitoring. The difference was larger than we expected. This post shows the numbers.
Footfall isn't just a traffic number. When broken down by zone, time-of-day, and staff coverage level, it becomes the one metric that connects marketing spend, staffing cost, and conversion performance.
Latency, cost, data sovereignty, and model update cycles all pull in different directions. This post sets out the decision framework we use with every customer during their architecture review.
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