Content Authenticity Scoring for Publishers & Brands

Companies buying guest posts, sponsored content, freelance articles and link placements increasingly receive machine-generated filler — and pay human prices for it. Detection tools built for academia (Turnitin et al.) misfire on marketing content, and single-document 'AI probability' scores are notoriously unreliable and legally fraught.

The honest version of this product avoids the detection arms race: instead of 'is this AI?', score publications and freelancers on verifiable authenticity signals — publishing velocity anomalies, topical coherence history, author-identity verification, engagement authenticity. Sell it as due diligence for link buyers and content procurement, where a risk score (not an accusation) is actionable.

Content and link buyers can't verify what they're purchasing; publishers flooding sites with AI content devalue everyone's spend. Trust infrastructure for the content-commerce layer doesn't exist.

SEO/content leads buying placements at scale, link-building agencies protecting their inventory quality, affiliate site operators vetting freelancers.

AI content flooded the content-commerce supply chain 2023–26 while Google's quality updates made low-quality placements actively harmful — buyers now lose money twice on bad content.

AI detectors (unreliable, wrong framing), manual vetting (doesn't scale), marketplace ratings (gameable). The publication-level risk-scoring framing is differentiated but must overcome deep buyer skepticism from detector disappointments.