You've heard this one before, right? An AI team ships something to production. Looks good in testing. Looks fine in staging. And then, six weeks later, customers are calling with complaints. By that point, thousands of bad decisions have been made, refunds are flying out the door, and suddenly everyone's scrambling in crisis mode.
It happens constantly, and here's why: without evals, the time between shipping and discovering failure is absolutely brutal. With proper testing, you'd catch it in maybe 6 minutes. Without it? You find out when customers bang on your door. That's 6 weeks of compounding damage.
And that gap between "we should have caught this" and "customers found it for us" is where the real money bleeds out.
The Case Study: The $500K Hallucination Disaster
What Happened
A document management SaaS company added AI-powered summaries to their product. Pretty simple feature. Upload a doc, AI reads it, spits back a summary. Users loved it. Within weeks, 15% of their customer base was using it.
The team did some QA. They tested 50 documents by hand. Everything seemed fine. Ship it.
The Problem Emerges
Three weeks in, support tickets start piling up. Customers are saying the summaries are wrong. Not just a little off. They're saying things that straight up aren't in the document. Some summaries describe stuff as "happened" when the doc only mentioned it was planned. Others are just plainly false.
Turned out the model was hallucinating. Making up plausible-sounding stuff that sounds real but isn't. And it was doing this about 8% of the time. On a corpus of 100 documents, that's 8 bad ones. But with thousands of docs flowing through daily? Hundreds of hallucinations every single week, and nobody knew.
What It Cost
Discovery wasn't fast. It took 3 weeks because:
- Customers only reported obvious errors at first. Subtle ones just felt like edge cases
- Support had to manually cross-check complaints against actual documents
- The team needed time to reproduce it internally and confirm it was systematic
By the time they really understood what was happening, 18,000 summaries had been processed. About 1,400 of them were hallucinations.
The cleanup bill:
- Investigation and triage: $50K in engineer time just to figure out what was broken
- Regenerating everything: They re-processed all summaries with a better model setup. $80K in API costs
- Customer refunds: They had to refund people. $120K
- Revenue loss: 35% of people who'd tried the feature turned it off. Customers left or downgraded. That was $250K in lost ARR
- Trust damage: Forum posts about the failure made other customers nervous about AI features. Feature adoption stalled for months
Total damage: roughly $500K
How This Gets Prevented
If they'd had an eval system in place? Different story:
Why Detection Speed Changes Everything: The Gap Between Minutes and Months
Here's the thing: when you find a production failure matters more than almost anything else. Earlier you catch it, cheaper it gets. Much cheaper. Here's the curve:
The math is relentless. Every day you wait means the problem compounds. With evals, catch it in 6 minutes. Without them, customers find it. That's 6 weeks of damage you can't undo.
Breaking Down the Pattern: What We've Learned From Real Failures
We've looked at about 30 LLM failures across our client base. Same patterns show up every single time:
| Failure Type | Discovery Time (No Evals) | Discovery Time (With Evals) | Cost Gap |
|---|---|---|---|
| Hallucinations/Bad Facts | 3-6 weeks | 5-6 minutes | $300K-800K |
| Biased or Unfair Output | 2-8 weeks | 20-30 minutes | $1M-5M+ |
| Quality Degradation | 4-12 weeks | Caught instantly | $200K-1M |
| Specific Input Type Failures | 1-4 weeks | 1-2 hours | $50K-250K |
Why does evals catch bias faster? Because it's systematic. Same with hallucination. These aren't random glitches. They're patterns. But customers only surface them naturally after weeks of hitting them.
The Defense System: Layered Protection Before Failures Happen
Teams that don't blow up in production have structured their defenses like this:
The insight: you won't prevent everything, but you can keep most of it away from customers. That's what evals plus monitoring plus automation buys you.
The Math: When Evals Actually Pay for Themselves
Here's what a real eval program costs and what it saves:
But here's what makes this click: if you skip evals, you're not saving $300K. You're betting that luck will work out. And when a failure hits, it'll be worse than any eval investment.
The Real Cost of Skipping Evals
This is counterintuitive but true: not building evals isn't free. The cost just gets delayed and amplified. You pay upfront ($300K in prevention) or you pay later ($500K+ in crisis). Pick one.
Prevention is boring and predictable. Recovery is chaotic and expensive. Recovery also tanks customer trust, which is hard to rebuild.
We've looked at eval adoption across our client base. Teams with evals fail way, way less often than teams without them. It's not even close.
Getting Started: If You've Already Shipped Without Tests
If your system's been live but you haven't built evals, here's the urgent roadmap:
- Week 1: Find the risky stuff. What's costing you money or hurting customers?
- Week 2-3: Write baseline tests for those pieces
- Week 4: Run those tests on your current live models. Might find something. Probably will
- Week 5+: Hook evals into deployment so everything new gets tested
That week 4 audit is uncomfortable. You're about to find problems in production. Better you find them on purpose than your customers do by accident.
Conclusion: There's No Getting Around It
Skipping evals is expensive. Skipping evals then having it blow up in your face and explaining it to the board? That's more expensive.
The simple version: $300K invested in evals stops $500K-2M in failure costs. That's not complicated. That's arithmetic.
Build evals. Monitor them live. Set up automatic rollbacks when something smells wrong. The alternative (not doing this) costs millions in wasted time, refunds, and lost trust that you won't get back.