Sell an auditable unlearning verification report to ML teams who need compliance evidence before shipping a ‘forgotten’ model.

Customer: A solo ML engineer at a 10-50 person AI startup who owns the model lifecycle and gets tagged in every GDPR deletion ticket — technically strong, no dedicated safety team, needs a paper trail fast.

Problem: After running an unlearning method, they have no credible, third-party-style evidence that the knowledge is actually gone versus merely suppressed by fine-tuning. Reviewers, legal, and clients ask ‘how do you know?’ and they have no good answer.

Pricing: one-time — $800 in month 3 (mix of 6-8 one-time report purchases at $99–$149 each)

Why now

GDPR Article 17 enforcement is tightening, and the recent wave of machine unlearning benchmarks (MUSE, RWKU, etc.) has created a vocabulary regulators and clients are starting to adopt — but teams still lack a turnkey audit artifact. The gap between ‘we ran an unlearning method’ and ‘here is documented probe evidence’ is exactly where liability lives right now.

Go-to-market

1. Post a free open-source version of the CLI on GitHub and write one detailed HuggingFace blog post walking through a real unlearning audit (e.g., Harry Potter dataset removal) — target ML Twitter/X and r/MachineLearning for distribution
2. Add a ‘—export-report’ flag that generates a signed PDF audit artifact (model hash, probe suite, pass/fail per knowledge category, timestamp) — this is the paid unlock at $99/run via a simple Stripe payment link in the CLI output
3. DM 20 ML engineers who have publicly posted about GDPR compliance or unlearning on LinkedIn/Twitter — offer a free audit run of their model in exchange for a testimonial quote
4. List on Poe, HuggingFace Spaces, and post to the EleutherAI and Alignment Forum communities where safety-conscious ML practitioners already hang out and have unlearning pain

Moat (or lack thereof)

No real moat. Any capable ML engineer could replicate the probe suite in a weekend. The advantage is being first to define the ‘standard’ audit format in a space where no format exists yet — early adopters shape the template that compliance teams start requesting. That’s a weak but real head start, not a defensible barrier.