Provenance & Evidence
Trace engineering values back to their source.
Give autonomy a clear operating boundary.
Make every action, result and decision reviewable: policy governs what can happen, people retain authority where it matters, and evidence stays connected to its source.
Authority, provenance and access are evaluated as each action happens. Governance stops being a retrospective audit exercise and becomes a property of the run itself.
Trace engineering values back to their source.
Explicit approval before consequential execution.
Control what AI can observe, propose and execute.
Identity, organisations, SSO, entitlements and controls.
Connect storage, PLM, lab and engineering systems.
Provenance is captured as work happens: which input, which model version, which solver settings, which prior result. A value in a report can be expanded until you reach the raw data behind it.
Policy states what the system may observe, what it may propose and what it may execute. Consequential actions stop at a gate and wait for a named person—there is no implicit escalation.
Identity, entitlements and engineering data come from your existing estate—SSO, PLM, storage, lab and test systems—so control is expressed once and applied consistently rather than re-implemented per tool.
Provenance is a side effect of running the work, so nobody spends the last two weeks of a programme rebuilding the paper trail.
Approval gates sit at consequential decisions, so autonomy speeds up the routine without quietly absorbing the judgement calls.
The same authority model governs connected, private and air-gapped deployments—including what may leave the boundary.
Produce evidence chains that stand up to a regulator asking where a number came from.
Bind requirements to the analysis, evidence and decisions that satisfy them.
Enforce classification and residency rules on every action, including exports and model transfers.
Share results without sharing the underlying IP, under entitlements you set.
Every observation, proposal and execution carries an identity, a policy and a timestamp.
Observe, propose and execute governed independently, per role and per environment.
Consequential actions wait at a gate. Nothing widens its own authority.
Illustrative platform targets. Actual outcomes vary by workflow, model and deployment environment.
“Trust is not a feeling about a model. It is a property you can demonstrate, action by action.”
We build for the moment a reviewer, an auditor or a regulator asks the hardest possible question—and the answer is already attached.
Meet Elidyne ↗We will show what the answer looks like when provenance, authority and approval are captured while the work runs.