A high-level overview of our on-device context intelligence architecture. Specific implementation details and validated performance metrics will be published after enterprise pilot validation.
All processing happens on-device. No video frames, images, or analysis results leave the device. Privacy is enforced at the architecture level.
No video frames, images, analysis results, or decisions are transmitted to any external server. The entire pipeline runs in device memory.
The inference engine will run fully on-device — model training begins once pre-seed funding closes. No network connection required, by design.
Model optimization for lower-power edge devices is in the research phase. Target: 2027.
The core component that transforms raw visual input into a rich semantic understanding of what is happening in the scene.
Multi-dimensional risk assessment that combines traditional pixel-based signals with context-driven modifiers to produce a nuanced risk score — not a binary classification.
Standard visual signals: skin tone, exposure, proximity. These are inputs to the risk model — not the decision itself.
Event type, relationship, platform. These adjust the interpretation of pixel-based signals dramatically.
What counts as harmful is defined by platform policy — not by a single universal threshold applied to all contexts.
We have validated our approach on prototype scenarios. We are not publishing specific accuracy, latency, or false-positive rate numbers until we have validated at scale with enterprise design partners and completed peer review. Numbers without context mislead. Fairness validation requires diverse populations. Benchmarks should be peer-reviewed.
Transparent accounting of what has been tested and what has not.