Key takeaways
- Flags damage, conditions, and unusable or missing captures.
- Suggestions only — a named reviewer always decides.
- Drafts the summary so write-up time collapses.
- Per-workspace toggles and spend guardrails.
What it looks for
Visible damage and condition signals — cracking, water staining, missing shingles, corrosion, dents, wear — flagged on the specific frame with a short description.
Completeness: whether every required step in the template actually produced a usable capture, and whether any frame is too dark, blurred, or obstructed to be evidence.
It drafts, a human decides
Every flag is a suggestion attached to the record, not a verdict. Reviewers accept, edit, or dismiss it, and their decision is logged.
This matters for regulated workflows: the audit trail shows a named person made the call, with the AI contribution visible as input rather than authority.
Summaries and write-ups
At the end of an inspection or support session, AI drafts a plain-language summary from the notes, flags, and OCR output. The reviewer edits it, and the approved text becomes the report's summary section.
For most teams this removes the slowest part of the job — writing it all up afterwards.
Cost and control
AI features run under per-workspace guardrails so spend stays predictable, and each capability can be disabled independently if your policy does not allow automated analysis.
Keep reading
People also ask
- Is our media used to train models?
- No. Inspection media is processed to produce your results and is not used to train third-party models.
- How accurate is the damage detection?
- Accurate enough to triage and prioritise, which is why it is positioned as reviewer assistance rather than automated adjudication.
- Can it work on historical inspections?
- Yes, analysis can be re-run over past captures in your workspace.
Need a hand with something more specific?
help@virtualinspection.ai