News & Insights
Milestones and short notes on building NV-diamond quantum sensors — the same cadence we keep on LinkedIn.
Before our first device is built, our sensor flies an end-to-end synthetic mission: magnetic terrain, a vehicle with its own interference, the full sensor model and the navigation filter. Every figure is honestly labelled model-derived — and our hardware milestones will be judged against the twin’s own predictions. The interactive twin is live; access is granted on request.
Every magnetometer flying today is a lab instrument strapped onto a vehicle, corrected by external compensation models dating back to 1953. We took the opposite path: design the sensor for the vehicle from the first principle — so the measurement stays true under way, with the platform’s own magnetic noise rejected on board.
Our modelling work is documented in eight public preprints on Zenodo — timestamped, DOI-referenced and citable. Publishing the reasoning behind the engine is part of how we build credibility: in the open, against the literature.
We’re now a member of NVIDIA Inception, the programme supporting deep-tech startups — a step that strengthens our compute and ecosystem foundation as we scale the SF-QSim engine.
Satellite navigation is increasingly jammed, spoofed and denied. A passive magnetic reference that emits nothing and cannot be switched off is the missing layer — and it is exactly what NV-diamond is good at.
Before a sensor reaches the cleanroom, it lives in SF-QSim. Simulating coherence and sensitivity across decoherence channels lets a lean team explore the design space at deep-tech speed.
Follow the build.
We share progress and insights regularly. Want the behind-the-scenes?