Incident simulation for AI customer service
And when it does, the damage doesn't stay where it started. It travels past the AI, onto the few people who take over, and out to the customers left on hold.
Every service in that chain was technically up. That is the kind of failure a basic uptime check isn't built to see.
The gap
In a typical AI degradation of service, the first signals are the effects: a backed up queue, a ticket spike someone happened to notice, a customer complaint. By then, the fix is being improvised. A team that has rehearsed it recognizes the pattern in seconds.
Illustrative example
infrastructure alerts in 48 minutes, in this example. Every service was up, responding, and returning success the entire time.
calls unanswered after 48 minutes, in this example. The AI passed 17 of the 24 calls to 2 people who can clear about 12 an hour.
Illustrative timings from a modelled voice-channel degradation.
Inside the app
Pick a service and a failure, then watch it travel through your systems, onto the people who catch what the AI drops, and out to the customer. Every run ends with the fallback that saves it and the control still missing.
AI voice support
Assumed peak: 80 calls a minute · example topology
Checkpoint · who needs to know
Named roles and timing are in the app.
Each run follows the failure one step at a time.
Ready
Press "Run failure" to begin
Who needs to know now
What the customer sees
People absorbing it
These panels fill in inside the app.
Where this run ended
Where it landed on the fallback ladder
The finding, the missing controls and where it lands on the fallback ladder are in the app.
See plansHow it works
Start from one of our service types, or model your own: the channel your customers use, the AI behind it, the data it reads, the systems it touches, and the people who catch whatever it drops. You describe it. We never plug into it.
Choose a failure (a slow provider, a leaked source, an unauthorized action, an overwhelmed team) and watch where it goes. Most of these do not take the service down. They make it quietly wrong, unsafe or expensive while it keeps answering.
Most tools stop at the system. This one follows the failure to two groups: the customer who feels it, and the staff who absorb it. When the AI hands work back, it lands on a queue, and the people on that queue get tired. Nobody budgets for that. We show it.
Every run ends with a plan, not just a red box: which fallback holds, who has the authority to pull it, who needs to know at each point, and how to tell customers in a way that keeps the focus on getting it fixed fast.
Pricing
You pay for how closely the model matches your service, and how much of the rehearsal we run with you.
See what happens when a standard AI service fails. Our service types, four failure types today, nothing to connect.
Model your own service instead of ours: your parts, your people, your numbers.
Your scenarios built for you, and your team taken through them live.
Prices in USD, plus applicable taxes. Your first subscription comes with a 30-day money-back guarantee; see the refund policy. Plans renew automatically until you cancel.
The taxonomy
A slowdown is the failure everyone expects. The others can happen while the service keeps responding, which is why uptime monitoring may not catch them. Here are four to start.
The model provider slows past your time budget or fails outright. Voice agents talk over callers; checkouts stall mid-payment.
Tell: response time over budget
The AI takes actions beyond its authority (refunds, emails, cancellations, access changes) because it had the permission and nothing stopped it.
Tell: actions with no approval trail
Private or internal information shows up in a response or a log. Once it's on someone's screen it cannot be taken back.
Tell: restricted content leaving its channel
Everything the AI can't finish lands on a person. The queue grows, the same people stay on it for hours, and mistakes follow. When people talk about "AI fatigue" they usually mean being worn out by new tools. This is the other kind: the people inside the automation, absorbing its failures, and no dashboard counts what that costs.
Tell: backlog and hours-on-queue rising together
More are coming to the app, including prompt injection, quality drift and biased outcomes. Join the list to get each one explained as it lands.
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Real AI failures, what they cost, and how a team could have caught them sooner. Plus early access when new features ship.