AI Proposes, the Simulator Verifies

What building SentinelTwin taught me about using deterministic simulation as the foundation for AI-assisted physical security planning.

Most camera setups create an illusion of protection.

A camera mounted on the wall may technically cover the sales floor.

But once you account for shelves, pillars, lighting conditions, camera angle, blocked views, and device failures, the footage may not be useful for the one thing that matters during an incident:

understanding what actually happened.

This is not only a camera problem.

We already have good cameras.

We already have security software.

The harder problem is verification.

Can you know before an incident whether the current camera layout actually covers the areas that matter?

That is the problem I explored while building SentinelTwin for the AI Builders Hackathon by Outskill and OpenAI.

What SentinelTwin does

SentinelTwin is a physical security simulation workspace with an AI layer on top.

You create or load a site layout, place cameras, lights, obstructions, security zones, and movement paths.

Then you run coverage analysis.

The system is designed to answer practical questions like: