Industries
Any software or hardware system that runs AI. Not a list of three industries.
This isn't scoped to drones, defense, or robotics, those are simply where we've validated deepest. The trust layer attaches to any software or hardware product that has an AI model making decisions inside it, on any sensor type, any output format, any industry. The pattern is the same everywhere it matters most: an AI model deciding something far from a human operator, where a silent failure is more dangerous than a loud one.
Where we've validated the deepest
Drones & robotics
Perception integrity with a real safety gate in the control loop, not a log line after the fact.
- Reliability score gates the controller directly, a confirmed fault can hold position or pause before a bad inference is ever acted on.
- Runs alongside real telemetry (GPS quality, battery, IMU, joint torque) as additional independent evidence.
- Local self-healing works without a persistent uplink, a disconnected unit keeps detecting, buffering, and recovering, and syncs when it reconnects.
Defense & ISR
Black-box by design: your model's weights, gradients, and internals never have to leave your control.
- Decision-making reads only a model's outputs, embeddings, boxes, confidence, so it can watch systems you can't or won't expose.
- Weight-integrity hashing and rollback ladders give an auditable, tamper-evident trail for every model state a unit has run.
- Escalation-over-looping: a persistent fault raises a human alert instead of retrying indefinitely, which matters when the cost of a wrong autonomous action is high.
Industrial automation
Local self-healing keeps a line or a warehouse fleet moving without a round trip to the cloud.
- The trust layer's own healing, retraining, validating, rolling back, happens in-process on real buffered data, with zero dependency on a cloud round trip.
- A bounded, validated rollback ladder means a bad update can always be reverted to the last known-good state.
- Fleet-wide learning is optional, not required, a single unit is fully capable of detecting and healing on its own.
Also a fit, by design not by accident
Anywhere an AI model's output feeds a real decision.
We've built and validated the deepest against perception models on physical, moving systems, that's where a wrong decision is hardest to undo, so it's the strongest proof. The trust layer itself only needs three things from any model: an embedding or feature representation, a confidence or probability output, and a decision or prediction, which is a very short list to meet.
Autonomous vehicles & maritime
Perception and control models where a wrong decision has no undo.
Smart infrastructure & energy
Grid, water, and building-automation models operating unattended for months.
Logistics & warehouse robotics
Fleets of pickers, sorters, and AMRs that must keep moving through drift.
Agricultural robotics
Field AI running for entire seasons with no connectivity guarantee.
Financial & risk models
Decision models where a silent drift is a compliance event, not just a bug.
Any inference-serving system
If it emits an embedding, a confidence score, and a decision, it's a fit.
Don't see your exact use case? The architecture is deliberately domain-agnostic.
Anything that emits an embedding, a confidence score, and a bounding box, class, or decision can be monitored, regardless of sensor, model family, or programming language.