I’m exploring a new venture at the intersection of content provenance, fingerprinting, and AI usage attribution (audio initially, potentially expanding to voice, animation, and other media). The problem we’re evaluating is how to reliably attribute creati

I’m exploring a new venture at the intersection of content provenance, fingerprinting, and AI usage attribution (audio initially, potentially expanding to voice, animation, and other media). The problem we’re evaluating is how to reliably attribute creative works when they are used, transformed, or learned by AI systems not enforcement or takedowns, but provable attribution and auditability that could later support licensing or royalties. Before building anything, we want to have a deep technical conversation with someone who has hands-on experience in areas like: • Audio or media fingerprinting / perceptual hashing • Robust similarity detection under transformations (compression, pitch/tempo changes, partial usage, synthesis) • Watermarking vs fingerprinting tradeoffs • False positives / false negatives in real-world systems • What is actually feasible vs academic or marketing claims This is not a job post (yet). We’re looking for paid consulting or advisory conversations to sanity-check an MVP approach before we pitch a pilot to talent agencies or studios. If you’ve worked on fingerprinting systems (music, video, speech, ML training detection, etc.) and are open to a conversation, I’d love to hear from you feel free to email me @ dmschultz1964@gmail.com Thanks in advance.