AI That Understands Physics.

Astrophysics & 3D Reconstruction

OMEGA transforms 2D telescope imagery into volumetric 3D structures — revealing the true physical depth and morphology of nebulae, galaxies, and stellar nurseries. It works by extracting spectral invariants from multi-channel FITS data, then computing physically meaningful depth maps using inter-channel energy relationships that conventional AI pipelines discard entirely.

Unlike machine-learning approaches that require labeled training sets of 3D structures (which don't exist for deep-space objects), OMEGA derives structure directly from the physics encoded in the light itself. Multiple demonstrations are publicly available on YouTube.

Image Forensics & Deepfake Detection

Cerebus provides a 284-dimensional forensic fingerprint for any image — a physics-based signature that captures the intrinsic physical properties of how light interacted with a sensor. Deepfakes and AI-generated images leave invisible but mathematically detectable traces in these spectral invariants, even when they are perceptually flawless to the human eye.

Because the fingerprint is derived from physics (not statistical training on known fakes), Cerebus is inherently robust to novel generation techniques. It doesn't need to have "seen" a new deepfake method before — it detects the absence of physical consistency.

Autonomous Vehicles & ADAS

Trident is a No-Reference Video Quality Assessment (NR-VQA) engine designed for safety-critical autonomous driving pipelines. It evaluates the physical integrity of camera feeds in real-time — detecting degradation, sensor artifacts, and environmental corruption before they compromise downstream perception algorithms.

Traditional VQA methods require a "ground truth" reference frame. Trident needs none — it derives quality metrics from the spectral physics of the feed itself. Pre-alpha benchmarks show a +91.9% accuracy uplift over conventional methods, positioning it as a critical safety layer for any ADAS or autonomous navigation stack.

Digital Pathology & Cancer Diagnostics

CYTOISED applies topological data analysis to histopathology — detecting sub-micron cellular anomalies by extracting Betti numbers and persistent homology features from stained tissue slides. It identifies malignant structures by their geometric and topological properties, not by pixel-level pattern matching against a training database of known cancers.

This makes CYTOISED fundamentally stain-invariant: it works regardless of the staining protocol, slide preparation, or scanner used. In an industry where inter-lab variability is a major source of diagnostic error, physics-based feature extraction eliminates an entire category of failure modes.