Robots that learn
without sharing
what they see.
Your robot sees everything. WRAITH lets it contribute to shared intelligence without ever transmitting a single frame of what it observed.
A role for everyone.
Enroll a Robot
Your robot trains locally and contributes encrypted updates to the shared model. Raw perception never leaves the device. Earn WRTH + USDC for every accepted round.
Keep the Model Honest
Stake WRTH and review contribution quality. Catch model poisoning, earn protocol fees, and become the immune system of the robot-intelligence network.
Start validating →License the Intelligence
Browse skill-models trained on diverse real-world robot experience — with full privacy provenance showing no raw footage was ever collected.
Browse models →Six layers of
defense-in-depth.
Local Training
Robots train on their own hardware. Raw sensor data never leaves the device — not encrypted in transit, simply never transmitted.
TEE Aggregation
Encrypted updates combine inside Intel TDX enclaves. No individual contribution is ever decrypted outside the attested environment.
Differential Privacy
Calibrated noise applied to the global model. No robot's environment can be reverse-engineered — a formal mathematical guarantee.
Traffic Shaping
Padded and scheduled uploads defeat traffic-analysis attacks. Observers cannot infer robot actions from encrypted network patterns.
Anti-Poisoning
Staked validators review every contribution. Model poisoning attempts result in stake slashing; honest validators earn protocol fees.
Confidential Settlement
Encrypted balances keep reward flows private. On-chain data reveals nothing about who contributed what.
Privacy is
the unlock.
A hospital, a homeowner, an enterprise fleet manager can verify continuously that nothing the robot saw was ever transmitted — only mathematics about what it learned.