Privacy by architecture.
Verifying machines means handling sensitive data from vehicles, factories and critical infrastructure. We designed Vantage so the platform gets smarter from everyone's data without anyone's data being exposed. Not as a policy promise, as a property of how the system is built.
Your data trains the model without leaving home.
Most AI gets better by collecting everyone's data into one giant pile. That pile is also one giant target. We do the opposite.
Vantage uses federated learning. Each asset's model learns locally, where the data lives. To improve the shared, global models that power cross-asset deployments (commercial fleets and OEMs), silos send only what was learned, never the raw data, and never anything that ties a user to a specific dataset. The global model gets the benefit of the collective without ever seeing the individuals.
So even in the worst case, a leak, there is no central pile of your data to lose.
Four layers, each doing real work.
We are deliberate about not overstating this. Here is what actually protects what you share, layer by layer.
Encryption in transit
Anything that crosses the network is encrypted with standard transport security (TLS/mTLS). A network-level leak gives an attacker scrambled data they cannot read without the keys.
Differential privacy on what's shared
What moves between silos is not raw data, it is the model's learning updates, with mathematical noise added. Even if those updates are captured, they cannot be reverse-engineered back into a specific user's data. This is a formal, provable guarantee, not a hopeful claim.
Secure aggregation
When many silos contribute, the aggregator only ever sees the combined total of their updates, never any single contribution. Even the operator running the aggregator cannot tell which silo contributed what.
Hardware attestation Optional
Where deployments use Trusted Execution Environments, aggregation runs inside a hardware enclave whose code is cryptographically verified. A participant can confirm the aggregator is running exactly the intended code before sending anything. This protects even against a compromised operator.
Sometimes the data never leaves at all.
Federated learning protects data when it moves. But the strongest privacy is data that never moves in the first place.
Where the deployment includes edge-capable hardware, Vantage can run basic anomaly detection directly on the device. For those checks, raw telemetry stays local and never has to be transmitted at all. This matters when bandwidth is constrained, or when a deployment requires a local-first posture by policy.
Availability: on-device inference applies wherever edge-capable hardware is deployed. That is the default for commercial automotive fleets, and an option for other deployments that choose to add hardware. Most non-automotive deployments run as a software (PaaS) layer, where this does not apply.
Trustworthy even when sensors lie.
Privacy protects your data. Integrity protects the verdict. Vantage trusts no single sensor by default: readings are cross-checked against a consensus and against physics, suspicious or colluding sensors are isolated, and the system keeps working with honestly reduced confidence even when a portion of sensors fail or are attacked.
Same architecture, different peace of mind.
Your car stays yours
Your vehicle's data builds your model on your terms. It is never pooled into a central dataset tied to you.
Benefit without exposure
Gain from fleet-wide learning while keeping each operator's data isolated and provably private.
Verifiable, not vibes
Differential privacy and secure aggregation are formal mechanisms. Attestation lets you verify the code before trusting it.
We are happy to go deeper.
Security and procurement teams can request a detailed technical review. Drivers can read how we handle data in plain terms.