Detect fake damaged-on-arrival photos - automated image-forensics for returns

Detect fake damaged-on-arrival photos - automated image-forensics for returns targets a $6.0B = 2.0M online merchants x $3K ACV. Rationale: global merchant count targetable with mid-tier SaaS plans and per-claim fees, average ARPU estimated at $3K/year for tooling that reduces returns and chargebacks. total addressable market with medium saturation and a year-over-year growth rate of 12-18% annual growth in returns-related fraud solutions as e-commerce volume and visual claims rise.

Key trends driving demand: More user-submitted evidence in disputes -- customers routinely attach photos to refund claims, creating a consistent input stream for automated analysis.; Proliferation of mobile editing apps and deepfake tools -- easier to fake photos increases merchant need for automated verification.; Returns volume growth post-pandemic -- higher absolute number of claims raises operational costs for manual review.; API-first e-commerce platforms -- Shopify, BigCommerce and marketplaces provide hooks for quick integration into return workflows..