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Supply Chain — Live Demo

Trace every bolt.
Replay every decision.

50,000 vehicles. 200 suppliers. One defective brake batch manufactured and quietly cleared by inspection — 12 months before anyone knew it was defective. The bitemporal question: which vehicles were assembled with batch BRK-2024-0847 before we knew it was defective?

50K
Vehicles in scope
200
Tier-1 suppliers
3,200
Vehicles exposed
14 mo
Defect hidden
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The story

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Recorded status diverges from actual validity — the bitemporal gap is now visible.
Inspection Record (AT RECORDED)
Batch BRK-2024-0847 as recorded at this date
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Vehicles assembled (as known)
Risk classification
AT RECORDED '—' — what inspectors saw on this date
True Validity (AT VALID — current knowledge)
Batch BRK-2024-0847 as we know it now
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Vehicles exposed (true count)
True risk classification
AT VALID '—' — what we know today, regardless of when it was recorded
Supply chain tree: Supplier → Batch → Component → Assembly → Vehicle
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Querying…
Supplier Batch (normal) Batch (defective) Assembly Vehicle Plant
Batch BRK-2024-0847
Defective brake component batch
Vehicles exposed
Supplying plant
Recorded status
Assembly window
Field failures
Bitemporal gap
Recall trace
Run the full recall query
Show the Cypher running this demo (real, live, against supply_chain)
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Your supply chain has a BRK-2024-0847.

Every component, every inspection, every sign-off — replayable in bitemporal time. Know exactly what was known when. Reconstruct any recall boundary in minutes, not months.