See the damage before the dispute
On-device and cloud computer vision scoring damage severity with adjuster-ready overlays and QA sampling.
Scaling inspections means hiring more people—unless every photo becomes a structured data point.
The Challenge
A multi-state roofing and home-services operator was processing thousands of property inspections per month. Manual photo review slowed estimates, missed subtle hail impact on shingles, and generated rework when carrier desk reviewers disagreed on damage extent. Disputes consumed senior adjuster time, delayed settlements, and eroded margins on jobs that should have been straightforward. Field teams captured inconsistent photo sets—different angles, lighting, and labeling conventions per crew—making quality assurance reactive rather than systematic.
The Innovoco Solution
We deployed AI-powered inspection tools that guide technicians in the field and score damage severity in real time. The system produces adjuster-ready overlays with QA sampling that carriers can verify independently.
Phase 1 — Calibrate on labeled inventory
Curated a golden dataset of 12,000+ labeled roof and exterior images spanning hail, wind, and wear across shingle, tile, and metal substrates. Established precision and recall targets by damage class and trained edge-optimized models for on-device guidance alongside cloud models for final scoring.
Phase 2 — Field deployment with QA loops
Rolled out guided capture on technician devices: real-time bounding boxes prompt re-shoots for under-exposed or off-angle frames. Cloud scoring produces severity heatmaps and measurement overlays. Senior adjusters review a configurable sample; disagreements feed model retraining quarterly.
Key implementations
Guided photo capture
On-device models prompt technicians for missing angles, flag blurry frames, and enforce minimum photo-set completeness before submission.
Severity scoring with overlays
Cloud-based AI models classify damage type and severity, producing annotated images with measurement assists that attach directly to estimate packages.
QA sampling framework
Configurable review rates by damage class and confidence band ensure human oversight scales with volume rather than growing linearly.
Carrier-ready export
Structured JSON plus annotated imagery packaged for Xactimate, Symbility, and custom carrier portals—reducing back-and-forth on evidence.
Drift and retraining pipeline
Monthly accuracy reports by region and substrate; disagreement logs between model and reviewer feed quarterly retraining cycles.
Technical Innovation
The system works in the field even without connectivity—guiding technicians on-device for immediate feedback, then syncing to the cloud for detailed scoring when connected. Reviewer feedback drives continuous improvement without disrupting production workflows.
Impact
- 50% faster inspections from guided capture and automated severity scoring.
- 99.7% detection accuracy on high-severity damage classes after two retraining cycles.
- 32% reduction in carrier disputes driven by consistent, annotated evidence packages.
- Scalable QA—senior adjuster review time grew 15% while inspection volume doubled.
Field teams capture better evidence in less time; carriers receive structured, verifiable packages; and disputes drop because both sides see the same annotated imagery.
Explore this outcome on your stack
We map scope, guardrails, and rollout to your data boundaries and teams—practical next steps, not a generic slide deck.
60 min · Free · No obligation
More case studies
Explore adjacent patterns from the same production playbook.
AI-Powered Business Intelligence
Ask questions in plain language, get governed answers with charts — no analysts, no ticket queue.
Read more
Customer support & voice
Triage, sentiment-aware routing, CRM-grounded answers at scale.
Read more
Regulated onboarding (KYC / AML)
Document intelligence, exception queues, immutable evidence for auditors.
Read more