At SDTH 2026, run by the Defense Venture Lab at NUSX, teams building on DomeCommand took first and third place in the Lord of the Rings integration challenge: an acoustic mesh and an interceptor drone under one command loop, and a civil defence app for Singapore built on the simulator.
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Inturai has signed a definitive agreement to acquire DomeCommand. Meanwhile we have been quietly integrating the two products: radio sensing from Inturai, flight path and spatial context from DomeCommand.
A cheap drone forces a response that costs a thousand times more, the operator is drowning in sensor feeds with too few good data points, and today's decision support is often thousands of hardcoded rules. DomeCommand is being built to change that, with a solver and an agentic harness instead.
Inturai Ventures has signed an exclusive letter of intent to acquire the technology behind DomeCommand. A short note on what Inturai's sensors do, and why sensors that can be carried are the part worth paying attention to.
Most sensor fusion quietly assumes the inputs are clean and the sensors sit still. Neither is true. Here is how DomeCommand models a picture that is honest about what it does not know, and then sends a drone to go find out.
Sensor fusion sounds like addition. It is not. A tick-by-tick trace of one drone seen by four sensors, the moment three of them agree an object is there and none can say what it is, and the rule that keeps the picture honest.
The data you most need to build a counter-swarm system is the data you can never collect, so we generate it. A look at the simulation engine: scenario generation across fleet and threat catalogs, why determinism matters, and how we are using engagement history to train the planner.
Two jobs, and everything else is detail: work out where you are, then work out what the motors should do about it. A ground-up explainer built on the cascaded controller I wrote for Udacity's Flying Car Nanodegree.