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GeoFiber

Patent-pending geometric inference architecture: peer-learned normal, dimensional explainability, calibrated bounds.

U.S. Provisional Patent Application · 50 Claims Across 3 Families

Inference across the geometric manifold.

Traditional AI anomaly detection relies on brittle static thresholds or black-box neural scores. GeoFiber takes a fundamentally different mathematical approach: mapping entities onto a high-dimensional geometric manifold, learning normative curvature from peer cohorts, and providing mathematically provable explanations for every observed deviation.

Core Architectural Properties

Operational Frontiers

Proven across mission-critical dual-use environments and enterprise intelligence pipelines.

MISSION & SYSTEM HEALTH

Fleets of nominally identical aircraft, spacecraft, and vehicles never behave identically. GeoFiber learns each asset's expected envelope from its peers and surfaces the tail number drifting from the fleet — early enough to act.

ASSURED AUTONOMY & EXPLAINABLE DECISIONS

Autonomy that cannot explain itself cannot be accredited. GeoFiber's outputs carry their reasoning and a calibrated confidence statement, aligning directly with defense and mission-assurance requirements.

SCIENTIFIC DISCOVERY

High-dimensional observational data hides structure that summary statistics average away. GeoFiber surfaces the entities, cohorts, and interactions that don't fit the population.

ENTERPRISE OPERATIONS

Commercial deployments in clinical workflows, revenue cycle operations, and industrial reliability. Plug in your data; curvature finds what your dashboards miss.

Licensing & Research Partnerships

The full 50-claim patent specification, peer evaluation benchmarks, and integration SDK are available under bilateral non-disclosure agreements.