chats, messages, or generations your AI produces monthly
wrong, unsafe, hallucinated, or off-scope: industry ranges run a few % to double digits
most bad answers are shrugged off; some become a ticket, refund, or churn
support time + goodwill + occasional refund/churn, blended
Estimated annual exposure
$162,000
Costly incidents / month300
Monthly exposure$13,500
Recovered by a gate that catches ~80%$129,600/yr
Illustrative only. A CI eval gate can't catch everything, and your real rates differ. That's exactly what a mini-eval measures. The exposure is almost always larger than the cost of testing for it, regardless of the exact figure.
is this worth it for you yet?
A 10-second fit check.
Worth it if…
- You ship an LLM feature — chatbot, agent, RAG, generator — to real users
- A wrong or unsafe answer has real cost: support, refunds, churn, brand, compliance
- You can't currently prove a prompt or model change didn't make things worse
- You want to move faster without the 3am "did that update break it?" fear
Not yet if…
- You don't have an AI feature in production (or in a near-term roadmap)
- It's a purely internal experiment with no users and no downside
- You already have a mature eval gate in CI that your team trusts
- You need a full product built first. That's a different conversation (happy to have it)
Want the real number instead of the estimate?
A free mini-eval measures your actual bad-response rate on your live feature. Or book a call and we'll scope the gate that closes the gap.
You're looking at an estimated $162,000/yr of exposure. The mini-eval turns that estimate into your real number.