Damage intelligence

Locate, classify and grade visible exterior damage.

Turn a guided photo set or walkaround into damage masks tied to named body parts, ready for triage, handover or review.

For Claims, rental, fleet, auctions, logistics and dealership operations

Exterior damage inspection illustration
/v1/damage/detectper image

The result

Built around an operational outcome.

Every result remains traceable to a mask, a body part and the exact source frame a reviewer can reopen.

  • Scratches, dents, cracks, rust and paint defects localised on the source image
  • Every detection tied to a named exterior part
  • Severity and repair-pricing inputs available as separate auditable stages

Included capabilities

The real calls behind the solution.

Use the complete workflow or call each stage independently. Every capability below maps to a production endpoint.

Damage detection

Return typed masks for visible exterior damage instead of a single opaque condition score.

POST /v1/damage/detect

Part mapping

Associate each detection with a door, bumper, fender or other named component.

POST /v1/parts/segment

Severity grading

Grade individual detections so your own rules can fast-track, refer or compare them.

POST /v1/damage/severity

Repair pricing input

Combine observed damage with your rate card to produce an explainable estimate.

POST /v1/pricing/repair

Implementation path

From capture to your existing system.

  1. 01

    Capture

    Collect a complete exterior set or index a walkaround video by vehicle position.

  2. 02

    Map parts

    Build the body-panel map that keeps every result attached to a named component.

  3. 03

    Detect and grade

    Locate visible damage, type it and optionally run a separate severity stage.

  4. 04

    Decide

    Feed structured results into your triage, charge, claim or arbitration rules.

Boundary

The models report visible evidence. Coverage, liability, fair wear and repair approval remain decisions made by your rules and your reviewers.

Test exterior damage inspection on your own archive.

Start with real source material and agree on the output before planning an integration.

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