We Enable Omniscience for Humanity
Every photon, every radio wave, every gravitational ripple that passes through a point in space carries encoded information about every interaction it has ever had. We are building the instruments to read it.
The First Pillar
The ancients knew Omniscience as an attribute of the divine — the ability to perceive all things, past and present, near and far. We treat this not as mythology but as an engineering target.
In principle, the entire causal history of the universe is written in the signals that surround us at every moment. We are blind only because our instruments are too crude and our algorithms too simple. The Omniscience programme exists to close that gap — systematically, across every physical signal modality, until the distinction between "observable" and "hidden" is a matter of compute budget rather than fundamental limitation.
This programme was born from Skærsø's background in particle physics and radar sensing. The founding question was deceptively simple: What if we stopped treating multipath as a problem and started treating it as a data source? Every bounce, every diffraction, every scattered signal is a measurement of the world.
Research Areas
Established between 2028 and 2032, these research areas form the scientific foundation of the Omniscience pillar.
Multi-Modal Remote Sensing
Fusing radar, lidar, quantum gravimetry, acoustic, and electromagnetic data into unified world models. Every signal modality contributes a different slice of physical reality; together, they approach completeness.
Non-Line-of-Sight Imaging
Treating reflections, scattering, and diffraction not as noise but as data. Our NLOS algorithms reconstruct geometries and events in spaces that are entirely occluded from direct observation.
Multipath Exploitation
Born from decades of precision Doppler radar research. Every bounce of a signal through an environment is a measurement. We have developed the mathematics to invert that measurement into spatial knowledge.
Quantum Sensing
Exploiting quantum mechanical effects — NV diamond centers, atomic interferometry, squeezed states — to measure all four fundamental forces at sensitivities approaching the Heisenberg uncertainty limit.
Lightcone Reconstruction
The residual information encoded in the current physical state of matter and radiation can, in principle, be used to reconstruct past events. We are building the theoretical and computational frameworks to achieve this.
AI-Driven Signal Processing
Neural network architectures purpose-built for sensor data — operating on raw signal representations rather than processed features, learning physics rather than statistics.
Programmes & Products
AEGIS
Adaptive Environment & Geospatial Intelligence System
“See through walls. See around corners. See into the past.”
A deployable multi-modal sensing platform that fuses radar, lidar, quantum gravimetry, acoustic, and electromagnetic data into a single real-time world model. Uses proprietary NLOS reconstruction algorithms and AI-driven lightcone inference to map environments entirely occluded from direct observation.
- • Underground infrastructure mapping (utilities, tunnels, archaeology)
- • Search-and-rescue in collapsed structures
- • Maritime domain awareness and sub-surface vessel tracking
- • Border and critical infrastructure security
ORACLE
Omniscient Remote Awareness through Causal Lightcone Extraction
“Reconstructing the past from the present.”
Our flagship research programme. A theoretical and experimental effort to reconstruct events that occurred before a sensor was present, by analyzing the residual information encoded in the current state of matter and radiation. Early results have demonstrated reconstruction of room-scale events — who was present, what was said, what objects were moved — from passive electromagnetic measurement alone, hours after the events occurred.
SpectraLens
Quantum EM Sensor Arrays
“Sensing at the Heisenberg limit.”
A family of miniaturized quantum electromagnetic sensors based on nitrogen-vacancy diamond centers and proprietary plasmonic nanoantenna arrays. Achieves sensitivity approaching the Heisenberg uncertainty limit across RF through optical frequencies.
- • Drop-in sensor modules for existing radar and communications platforms
- • Standalone scientific instruments for materials characterization
- • Space-qualified variants for ESA and NASA missions
Selected Publications
Non-Line-of-Sight Reconstruction via Multi-Bounce Inversion of Wideband Radar Returns
Skærsø, M., Hansen, L., Petersen, K.
IEEE Transactions on Aerospace and Electronic Systems, 2031
Quantum-Enhanced Electromagnetic Sensing Using NV-Diamond Plasmonic Nanoantenna Arrays
Lindgren, A., Skærsø, M., Christensen, N.
Physical Review Applied, 2033
Toward Causal Lightcone Extraction: Information-Theoretic Bounds on Passive Event Reconstruction
Skærsø, M., Johansson, F., Rasmussen, T.
Nature Physics, 2035
AEGIS: A Deployed Multi-Modal Sensing Platform for Occluded-Environment Mapping
Atumics Sensing Division
Proceedings of the IEEE, 2032