Executive Summary

Many marketplaces, payment providers, brands and growth-stage creators struggle to vet the flood of promotional screenshots and testimonials that claim outsized results—manual review is slow, inconsistent and easily gamed, creating reputational and financial risk for buyers and platforms. This pain is acute at scale: there are roughly 15M online businesses/creators in a $22.5B addressable market, and increasing paid creator relationships magnify the fraud surface. You could build a multimodal AI vetting product that ingests screenshots, runs OCR and vision-language extraction to parse claims and metadata (handles, timestamps, pricing), cross-checks those claims against web sources and behavioral signals, and returns a risk score, provenance links and a human-in-the-loop escalation workflow accessible via API, dashboard and a browser extension. The product would prioritize precision for enterprise buyers while offering tunable sensitivity and analytics for platform policy enforcement. This is timely—platform and regulator pressure plus a high-market-score opportunity (92/100) mean marketplaces, payment processors and brands are primed to pay (around $1,500 ACV is plausible for mid-market customers) for automated vetting that reduces chargebacks and fraud exposure. You can differentiate by combining the latest OCR + vision-language models with specialized classifiers for “guru” claim patterns, fast provenance retrieval and enterprise integrations, but expect real challenges around adversarial/edited screenshots, privacy/compliance, and the need for labeled training data and continual model updates.

Market Opportunity

Identify fake online gurus from a screenshot using AI targets a $22.5B = 15M online businesses/creators × $1,500 ACV total addressable market with medium saturation and a year-over-year growth rate of 15% YoY — fraud prevention and digital trust tool categories growing as reported across industry analyses and increasing online commerce.

Key trends driving demand: Multimodal AI progress — better OCR + vision-language models make extracting claims and metadata from screenshots reliable enough for automated vetting.; Explosion of creator economy and influencer marketing — growth in paid creators creates scaling fraud opportunities that need automated vetting.; Platform and regulator pressure — marketplaces and payment providers are under pressure to reduce fraud, creating B2B demand for quick vetting tools.; Consumer distrust and education — high-profile scams have increased consumer demand for simple verification tools and third-party signals..

Key competitors include ScamAdviser, ZeroFox, Sift.

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Identify fake online gurus from a screenshot using AI

Executive Summary

Market Validation

Market Opportunity

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