Our product serves as a memory and failure prediction layer in physical AI. In its simple form, using the chat interface, you can ask your camera or home robot where you kept your car keys or the kids left the TV remote. In the wild, it is a layer that tells perception to resort back to its last memory of a path or place, when to execute a fail-safe routine or when to alert a human in the loop. We are currently at the technical documentation phase with multiple interested physical AI companies and pre-execution control and admissibility startups to achieve their regulatory or AI governance goals. Paid design partnerships or per-node deployment fees are the discussions we have had with them.
A physics-based human-auditable memory and reliability layer for physical AI that tells when a perception or localization system's confidence is degrading before that shows up as a bad decision downstream. In robots and drones, unlike chatbots, failure prediction is vital.