Deterministic neural compute
AI that doesn't guess. It learns, reasons, and cites its work.
A neural engine that runs on edge silicon — zero GPU required, byte-identical from one run to the next, and trained on unlabeled data. No hallucination. No drift. No cloud tax.
Neuromorphic One-class Vision Architecture
Your GPU knows cars.
This monitor flags everything else.
A learned normal model watches an event camera stream for 20 minutes of routine driving. After that, anything unexpected drops its confidence score within 33 ms of entering the frame. No labels. No training on anomalies. No GPU required at inference.

A neural server engine for
multiplayer games.
NullTick replaces the fixed 60 Hz tick loop with a neural scheduler that only fires when something changes. The same deterministic engine, applied to multiplayer networking: fewer CPU cycles, lower bandwidth, identical game state on every peer.
The industry's AI wall
Today's dominant AI paradigm — large stochastic models on server GPUs — has three fundamental problems that no amount of scale solves:
The substrate: a neural engine
that keeps its word.
Every KHALM product — KHALM AI, NullTick, and what comes next — runs on the same substrate: a learned neural model that is deterministic by construction, not by capping a stochastic system.
Built something that needs a brain that keeps its word?
We're heads-down building. If your application demands deterministic, auditable, edge-deployable neural inference — let's talk.
KHALM