Every defect, attested.
Physical assets degrade. Parts have defects. Qualification handoffs across the supply chain ride on inspection evidence — but that evidence is fragmented, paper-based, or trapped in vendor-specific tooling. Quality Inspection turns sensors into AI-driven condition, defect, and provenance signals. Vibration, thermal IR, ultrasonic, eddy-current, radiographic, optical, spectroscopic, CMM, electrochemical — any modality, any asset class. The output is portable, attested, and consumed downstream by regulators, insurers, lenders, and off-takers.
Sensors are the substrate. Robots and drones are not the wedge.
Inspection records today are paper, PDF, vendor-specific consoles, or trapped in single-instrument silos. AS9102 First Article Inspections live in spreadsheets. Material Test Reports travel as scanned attachments. Pressure-vessel UT scans live in the inspector’s laptop. The signal is in the data — and the data is not portable.
Quality Inspection consumes data from any sensor regardless of carrier — fixed, handheld, line-mounted, embedded, drone-borne. Per-modality AI models (physics-aware, not generic computer vision) output remaining useful life, severity scoring, defect classification, and anomaly detection. Cross-firm qualification handoff schemas — AS9102, PPAP, MTR, ASME Section V / VIII, IPC-A-610, AWS D1.1, API 510 / 570 — make inspection records portable across organizational boundaries. An append-only attested ledger lets insurers, lenders, and regulators consume condition evidence as a feed.
Three pillars of sensor-side intelligence
Per-modality AI. Portable handoffs. Attested ledger.
Inspection records have to do three things: classify what the sensor sees, travel with the part across firm boundaries, and seal into a ledger that downstream consumers — insurers, lenders, regulators, off-takers — can subscribe to.
Per-modality AI
Vibration / acoustic (time-frequency CNNs + RUL regression). Thermal IR (physics-constrained segmentation). NDT (phased-array imaging + 3D CT segmentation; reference flaw libraries — ASTM E2375 / ISO 17640 / ASTM E1742). Optical (anomaly detection on good-parts-only; supervised when labels available). Spectroscopy (chemometric foundation models spanning NIR / FTIR / Raman / LIBS / XRF / OES with calibration transfer). CMM (dimensional analysis against CAD nominal + GD&T). Foundation-model substrate shared with North’s Multimodal FMs.
Cross-firm qualification handoff
Canonical schemas for inspection records that travel with parts, batches, and shipments. Aerospace: AS9102 FAI, AS9100, FAA 8130-3 / EASA Form 1, NCAMP. Pressure: ASME Section V (NDT), Section VIII (vessel), API 510 / 570. Welding: AWS D1.1, ASME Section IX. Electronics: IPC-A-610, IPC-J-STD-001, IPC-7711/21. Materials: ASTM, EN 10204 (3.1 / 3.2). Pipeline: API 1163, NACE SP0102. Pharma: USP / EP, GMP batch records.
Continuous condition attestation
Append-only, tamper-evident ledger of asset condition over the asset’s lifetime. Audit substrate for regulatory inspections, insurance underwriting, capital-asset valuation. For high-stakes assets — pressure vessels, pipelines, tailings dams, wind turbine blades, aerospace structures, BESS cells — the ledger is the artifact lenders and insurers consume as a feed.
Inspection in action
From sensor pickup to sealed attestation in under 300ms.
An NDT scan, per-modality AI classification, and a sealed cross-firm-portable ledger entry — without leaving the line. Every defect is graded, every condition is attested, every record travels with the part.
280ms from sensor pickup to ledger entry sealed
Phased-array UT scan → physics-aware AI defect classification → cross-firm-portable attestation, all without leaving the line.
Sensors. Models. Schemas. Ledger.
Hardware-agnostic sensor adapters, physics-aware per-modality models, canonical cross-firm handoff schemas, and an attestation feed insurers and lenders subscribe to.
Bently Nevada / SKF / IFM (vibration). FLIR / Optris / Telops (thermal IR). Olympus / Sonatest / Eddyfi (UT / ET / phased-array). Cognex / Keyence / basler (vision). Hexagon / Mitutoyo / Zeiss (CMM). Bruker / Thermo (NIR / FTIR), TSI / Avantes (LIBS), Olympus / Bruker (XRF), Spectro / Hitachi (OES). We run on the instruments; we don’t make them.
Physics-aware per modality + asset class, not generic computer vision. Calibration tracked, drift-aware. Out-of-cal sensors flag inference as uncertain. Foundation-model substrate shared with North’s Multimodal FMs.
AS9102, PPAP, MTR, ASME V/VIII, IPC-A-610, AWS D1.1, API 510/570 — canonical schemas riding on Standard Engines’ provenance ledger and Multiplayer’s ZK substrate. Inspection records travel with parts across organizational boundaries.
Insurance underwriters, lenders, off-takers, regulators subscribe to continuous condition attestation per asset under coverage. The output becomes underwriting input for Theorem, attested-pool eligibility for Embedded Trading, and evidence for AI-enabled regulatory compliance.
Sensor-side intelligence. Portable attestation.
Run AI on any sensor, on any asset, with cross-firm-portable qualification records and an attested condition ledger — consumed by every regulator, insurer, and lender downstream.