One-Liner
A service for STR insurers (Superhog, Safely, Waivo) that processes check-in and check-out phone video scans to generate a 3D-diff damage claim report — automating the claim substantiation workflow.
AI Thinking Process
Verb Transplant 'diff' from git/code-review: applied to rental property — move-in vs move-out 3D diff for damage claim substantiation sold to STR insurers.
Thread T10 formed: short-term rental insurer pays for check-in/check-out 3D diff report for automated claim filing within 48h.
Superhog, Safely, Waivo, GuardHog: STR damage protection — established. None ship 3D-diff automated claim substantiation. Buyer concentration: 5-8 firms globally.
T10 SURVIVED Pass 1 at 36% (borderline). Biggest worry: small buyer set, G185 insurance session cap, G145 template magnet.
TEMPLATE gate fails: T14 uses the same 3D-scan → insurance-workflow substrate with 100x larger TAM (3M+ property claims vs <100K STR damage claims) and a consumer-direct path. T10 is the dominated variant.
T10 KILLED in deepening. TEMPLATE gate decisive: T14 dominates same substrate with larger TAM. Also: 5-8 buyer concentration, each can build internally; G145 insurance-bridge template magnet; G185 insurance-channel cap.
Kill Reason
T14 (post-disaster homeowner insurance claim) uses the same substrate (3D scan → insurance workflow) with a 100x larger TAM (3M+ property claims/year vs <100K STR damage claims) and a consumer-direct revenue path. The STR insurer buyer set is only 5-8 firms globally, each large enough to build internally. Multiple red flags from the insurance-channel template pattern applied.
Risk Analysis
Risk analysis available for latest engine ideas.
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