Prices an accuracy SLA you can actually back
Your customer wants an accuracy guarantee and nobody can price one
Average accuracy is 97.4% against a 95% promise, so the naive premium is zero. Fourteen weeks in the record breached anyway. Underwrite prices the tail, which is where every payout actually comes from.
The problem
Enterprise buyers now ask for a guarantee and every vendor improvises the answer
The deal stalls on a contractual accuracy commitment. Sales wants to say yes, engineering has a mean accuracy number, and neither has any idea what the commitment is worth. So the vendor either refuses and loses the deal, or agrees and finds out the cost during a bad quarter.
The insight
Payouts live in the tail and the mean says nothing about the tail
Insurance has priced this way for a century: model the shape of the extreme, not the middle. Fitting a generalised Pareto distribution to the accuracy shortfall gives the severity of a breach worse than anything yet observed, which is precisely the event a contract exposes you to. Frequency comes from the record; severity comes from the fit; the premium is expected loss plus a loading for the variance you are absorbing.
Generalised Pareto fit to the accuracy shortfall below the guarantee, return-level extrapolation to the contract period for severity, empirical breach frequency, and a loaded premium with a stated break-even count.
How it works
Four steps, no data science team
Accuracy per period, as far back as you have it.
The shape parameter tells you how bad a bad period can be.
Guarantee, payout, period, loading. Out comes a premium and a break-even.
Re-fitted continuously, so a drifting model reprices before it breaches.
Who it is for
The person who has to sign the accuracy clause
AI vendors selling into enterprises that demand contractual quality commitments, and the buyers asking for them.
Pricing
- –Tail fit
- –Breach frequency
- –Indicative premium
- –Continuous repricing
- –Drift alerts
- –Contract portfolio view
- –Break-even tracking
- –Backed policy
- –Claims handling
- –Portfolio reporting
- –Reinsurance
Competition
What exists, and what it does not do
| Who | What they do | The gap |
|---|---|---|
| Uptime SLA tooling | Tracks and credits availability commitments. | Availability is binary and observable. Accuracy is a distribution, and none of these price one. |
| Tech E&O insurance | Covers professional liability for software vendors. | Priced on revenue and industry, not on the actual error distribution of the model. It does not know what your accuracy tail looks like. |
| Emerging AI-liability insurers | Early products aimed at AI-specific risk. | The nearest real competitor and the most likely acquirer. Their gap today is that they underwrite on questionnaires rather than on the model output record. |
| Refusing to sign | What most vendors do. | Free, and it loses the deal to whoever agrees. |
The premium tier is an insurance business, which means capital, regulatory licensing, adverse selection and claims handling — none of which a solo founder can carry, and all of which make this the hardest thing in this document to actually build. The honest version is to sell the pricing tool and let a licensed carrier take the risk, which caps the upside considerably. There is also a genuine statistical exposure: the tail fit assumes the future resembles the past, and a model upgrade can change the error distribution overnight in a way no history predicts.
Market
Priced as a share of the contract value it unlocks, not as a software subscription
Small now and structurally growing with every enterprise AI contract that includes a quality clause. Genuinely nobody is doing this, and the reason is that it is hard rather than that it is unwanted.