Infrastructure for Photo/Video Damage Assessment & Estimation

Photo-based damage assessment requires explicit documentation of what damage severity levels trigger total loss determination, which image quality standards are required for AI estimation, and when physical inspection is mandatory despite digital submission. Claims manuals must formally define these thresholds because automated settlement offers generated from photos have regulatory and litigation implications. Without current, findable documentation, the AI generates estimates that contradict filed settlement procedures.

Computer vision damage assessment requires automated, structured capture of photos and videos from multiple angles, with metadata (timestamp, GPS, device type) automatically extracted and linked to the claim record. Each image must be tagged with damage zone, component type, and severity upon submission—not manually reviewed post-hoc. Automated capture ensures the AI receives consistently structured visual inputs with complete metadata for every claim, enabling reliable damage detection and repair cost estimation.

Damage estimation from photos requires formal ontology mapping DamagedComponent (Vehicle.RearBumper, Property.Roof.Shingles) to RepairOperation (Replace, Repair) to LaborCode and PartsCost from repair cost databases. The AI must link image-identified damage to Mitchell or Xactimate line items through machine-readable entity definitions. Without formal ontology, visual damage detection cannot translate to structured repair estimates that integrate with claims payment systems.

Photo damage assessment requires API access to repair cost databases (Mitchell, CCC One for auto; Xactimate for property), the claims core system (policy coverage and deductible data), and the computer vision processing platform. These connections enable the AI to receive submitted photos, compute damage estimates against current repair costs, check coverage limits, and write estimates back to the claim record without manual data transfer between systems.

Damage estimation accuracy depends on current repair cost data—Mitchell and CCC update parts prices and labor rates frequently, Xactimate pricing updates quarterly by region. Event-triggered maintenance ensures that when repair cost databases publish updated pricing, the estimation models incorporate current costs rather than generating estimates based on outdated rates. Stale repair cost data produces systematic underestimates during periods of supply chain inflation.

Photo damage assessment must integrate the image submission channel (mobile app, email), computer vision processing platform, repair cost databases (Mitchell/CCC/Xactimate), claims core system (coverage, deductibles, payment), and potentially the FNOL system (initial claim context). API-based connections between these systems allow the AI to receive photos, compute estimates, verify coverage limits, generate settlement offers, and write results back to the claim record within a single automated workflow.