Architecture
Edge vs. Cloud for Video Analytics: A Practical Decision Framework
Latency, bandwidth, privacy, and cost pull in different directions. Here is how to decide where each part of a Vision AI pipeline should run.
7 min readRead
Insights
Thinking in three dimensions.
Practical perspectives on computer vision, edge AI, and operating Vision AI responsibly at scale.
- Architecture
- Engineering
- Trust & Governance
- Product
- Engineering
What “Real-Time” Actually Means in Computer Vision Deployments
Frame rate, inference time, and end-to-end latency are three different numbers. Confusing them leads to systems that look fast in demos and fail in operations.
6 min readRead - Trust & Governance
Privacy by Design in Vision AI: Techniques That Work in Production
Privacy is not a policy document. It is a set of architectural choices that decide what data exists, where it lives, and who can see it.
8 min readRead - Product
From Detection to Decision: Designing Rules That Operators Trust
A model that detects well is only half the system. The rules layer determines whether operators receive signal or noise.
5 min readRead - Engineering
Measuring Model Accuracy Where It Matters: Beyond mAP
Benchmark scores rarely predict operational performance. Evaluate on your cameras, your conditions, and the decisions you actually make.
6 min readRead - Architecture
The Anatomy of a Multi-Camera Analytics Pipeline
From RTSP to a prioritised event: a walk through the stages that turn dozens of streams into one coherent picture.
9 min readRead
Perceive. Understand. Act.
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