Reading claims data as a defect signal

A support inbox full of warranty claims is a queue to clear. The same claims, filed against a structured registration and tagged consistently on the way in, are a defect signal — evidence about which products actually fail, and how.

The structure has to exist before the pattern can

A claim never attaches to an order or a product directly in our model — it attaches to the registration, the coverage record that already knows which line item, which rule, and which coverage window applied. That's what makes a claim comparable to every other claim against the same product, instead of a one-off support ticket.

• Claims triage classifies complexity, estimates validity, and flags anomalies the moment a claim is filed — a first-class actor in the workflow, not a chatbot bolted on the side.
• Assessment intelligence pre-populates a defect category and affected area from the claim description and product metadata, for a human to confirm or correct.
• Every AI decision writes a decision record — inputs, reasoning, confidence, model — so a human override becomes training signal rather than a discarded judgment.

Text today, not vision

The intelligence layer reads claim descriptions, product metadata, and claim history — it does not yet look at photos. That's a deliberate sequencing call: get the structured-text signal right first, since the same defect-category and anomaly-detection groundwork is what a future vision pass would need to build on rather than replace.

AI failures never block the underlying workflow. A claim gets created, transitions, and resolves whether or not the intelligence layer is reachable that minute — the manual path is the floor, not the fallback of last resort.

What falls out the far end

Rule Recommendations and Defect Pattern Detection run as periodic background analysis over a tenant's own claim history — never across tenants, by design — surfacing coverage gaps and quality trends a person would otherwise have to notice by hand from a spreadsheet.