BlurredBox AGD is the governance layer for mission-critical machine learning. It predicts failures before they happen and prevents them from happening in milliseconds.
Not observability. Real-time intervention — engineered for systems where failure is not an option.
Deep-learning models forecast drift and systemic failure before they breach policy — shifting governance from reactive monitoring to pre-emptive control.
A decoupled decision-and-enforcement design over a low-latency message bus delivers ~5ms intervention and load-balancer rerouting.
Signed identity resolution and immutable, hash-chained audit logging. Every governance decision is verifiable and tamper-evident.
While conventional tooling watches logs, BlurredBox AGD intercepts the execution path itself — a high-priority native enforcement layer that recovers from failure without the overhead of higher-level runtimes.
Low-latency messaging for non-blocking, high-frequency enforcement.
Deploy on-premise or in isolated clouds. Model weights never leave your perimeter.
Every decision cryptographically signed and hash-chained in an unalterable log.
Sovereign by design. Telemetry stays inside your perimeter; only anonymized, metadata-level anomalies required for governance are transmitted — over encrypted channels, fully audited. Enforcement is deterministic and reversible, with a complete chain of custody for every action.
We're accepting early institutional partners into a private pilot. Engagements are handled personally — no sales funnel, a direct technical conversation.
Designs the predictive neural network layer — processing live telemetry to forecast and halt model drift before it ever reaches a decision. Graduate of Picsart Academy's 15-month AI / ML program.
Architects the core C daemon and systems infrastructure, enforcing AI safety directly at the OS level — the sub-5ms rollback engine and the backend that carries it.