A hosted audit service that stress-tests model edits for reversal-curse failures before they ship to production.

Customer: ML engineer at a Series A–C AI startup who owns a RAG or fine-tuning pipeline and has recently started using ROME/MEMIT to patch factual errors in a deployed model without full retraining — typically solo or in a 2-person ML team, no dedicated safety hire.

Problem: They apply a knowledge edit, run a handful of forward-direction spot checks, and ship — not realizing the edit fails silently on reversed, negated, or paraphrased queries. The first sign something is wrong is a user complaint or a red-team finding, not their own QA.

Pricing: saas-mrr — $800 MRR in 4 months (8 paying teams at $100/mo)

Why now

Mechanistic interpretability research published in 2024–2025 has made the reversal curse a named, credible failure mode that ML engineers can now cite to their managers — giving them a reason to buy a tool rather than build one. ROME and MEMIT adoption is also accelerating as fine-tuning costs rise.

Go-to-market

1. Post a free open-source CLI on GitHub with a compelling README showing a real reversal-curse failure on a public model (e.g., Mistral + a ROME edit) — target HuggingFace forums, r/MachineLearning, and the EleutherAI Discord where model-editing practitioners already hang out.
2. Write one long-form post on LessWrong or the Alignment Forum framing the tool as a lightweight safety audit layer — this audience overlaps heavily with early adopters and will share it without prompting.
3. DM the 20–30 authors of recent ROME/MEMIT papers and model-editing blog posts on Twitter/X; offer free hosted audits of their edits in exchange for a quote or case study.
4. Launch a $49/mo ‘indie’ tier on a simple landing page (Stripe + FastAPI backend) covering up to 50 edits/month with a Streamlit report — price it low enough that a solo researcher can expense it without approval.

Moat (or lack thereof)

No meaningful moat. The core logic (generate reversed/paraphrased probes, score consistency) is reproducible in a weekend by any competent ML engineer. The only durable advantages are: (1) a curated, growing library of adversarial probe templates tuned to specific edit types, which compounds with usage data, and (2) being the first result when someone Googles ‘reversal curse testing tool’ — an SEO/brand timing advantage, not a technical one. If OpenAI or a well-funded safety org decides this matters, they build it in-house. Realistic ceiling is a small, sticky SaaS serving a niche before the problem gets absorbed into broader model evaluation platforms like Weights & Biases or Braintrust.