SOHM Research Labs

We build mathematical primitives that answer fundamental questions about data — before computation happens.

Our first technology is the Spectral Memory Kernel (SMK): a constant-memory signal validation primitive that determines whether incoming data contains structure worth processing.

The problem: AI systems waste billions in compute on noise, corrupted inputs, and unstructured data. Sensors fail. Signals degrade. Models run anyway.

Our solution: A mathematically rigorous gate that asks "Is there structure here?" — in constant memory, in real time, with no training required.

SMK is available for licensing and integration.

Patent pending (USPTO). Formally verified. IEEE submission under review.

Applications:

• AI data centers — pre-inference gating

• Robotics — sensor integrity validation

• Autonomous vehicles — real-time sensor trust

• Medical devices — signal quality assessment

• Power grids — SCADA/PMU validation

Los Angeles, CA