The maintenance agent records sensor history, inspection records, and the component provenance it relied on, along with the standard applied.
Solutions · Transportation
Replaces · Fragmented maintenance systems · Single-operator data silos
Verifiable AI for transportation.
Predictive maintenance powered by shared context across operators.
Rail and transit networks depend on predictive maintenance, but maintenance context is fragmented across operators and single-vendor systems. nOS connects component provenance, maintenance events, and cross-border tracking into shared context graphs, so agents generate trusted insights from multi-operator data. Trace Labs works with operators such as Swiss Federal Railways (SBB) to make maintenance decisions verifiable across organizational boundaries.
Decision traces enshrinedReal-time component provenance Maintenance decision rationale Sensor anomaly investigation chains Cross-border tracking and recall lineage
What gets captured
Most relevant memory layers
Shared & Verifiable
Working memory captures private agent reasoning. Shared memory is where multi-party context coordinates. Verifiable memory is where decisions are enshrined.
Deployment & partners
Swiss Federal Railways (SBB)
Real enterprise data, processed in production, with decision traces enshrined as Knowledge Assets in the OriginTrail DKG.
The problem
The case for verifiable decisions in transportation.
Rolling stock, track, and signalling assets outlive the organizations that maintain them. A component may be manufactured by one company, installed by another, serviced by a third, and inspected by a regulator that trusts none of their databases by default. Maintenance history is the asset, and it is scattered.
Predictive maintenance agents make this sharper. A model can recommend deferring an intervention, but the operator carries the safety case. Without a record of the evidence and the authority behind that recommendation, the organization is accountable for a decision it cannot fully reconstruct.
How it works
How a decision becomes verifiable.
Asset context that other operators and the entity in charge of maintenance are entitled to see moves into shared memory, so history follows the component rather than the company.
Deferral, inspection, and replacement decisions record their rationale, including the risk threshold used and the engineer who authorized any deviation.
The trace is enshrined in the DKG so a safety regulator, a leasing company, or the next operator can verify the maintenance reasoning independently.
What changes
What this changes in practice.
The change is that maintenance reasoning outlives the contract and the database. When an asset is sold, leased, or transferred between operators, the receiving organization inherits not just a service history but the justification behind each intervention, which is what a safety case actually rests on.
Internally it shortens the distance between a model recommendation and an accountable decision. The engineer who accepts or overrides a predictive recommendation is recorded alongside the evidence, so the organization can show a human exercised judgement rather than deferring to a model it cannot inspect.
FAQ
Questions about transportation.
Who is this for within a rail or transit operator?
Asset management, maintenance engineering, and safety teams that must justify interventions, plus the entity in charge of maintenance that carries regulatory responsibility for the asset.
What happens when an asset changes operator?
The decision history is attached to the asset rather than to one operator database, so the receiving organization inherits verifiable maintenance reasoning instead of a document dump.
Does this work with our existing asset management system?
Yes. nOS connects to the systems already in use. The asset register stays where it is; what nOS adds is the verifiable record of why each maintenance decision was made.
Is Trace Labs working with transport operators today?
Swiss Federal Railways (SBB) is named among the deployments where enterprise data is processed in production with decision traces enshrined as Knowledge Assets in the OriginTrail DKG.
How does this interact with predictive maintenance models?
The model produces a recommendation; nOS records what the recommendation rested on and who acted on it. That separation matters because models are retrained and retired while the safety case must remain explainable.
Is this only relevant to rail?
No. The same pattern applies wherever long-lived assets move between operators and regulators expect maintenance decisions to be justified, including transit fleets, aviation ground assets, and heavy logistics.
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