Enterprise SaaS · Cloud Platform
Two months before GA launch, ServiceNow's voice AI team had zero automated testing infrastructure. They were targeting hundreds of thousands of monthly calls, and manual contractor evaluation was never going to scale to production. There was no regression testing between releases. No load testing. And in competitive deals, no performance data to put in front of Fortune 500 buyers who wanted proof, not promises.
Then a deadline arrived. A customer conference demo gave the team a hard date, and competitive deals in flight required hard performance numbers the team couldn't produce. Building this infrastructure internally was a six-month project. They had one week.
Manual contractor evaluation couldn't scale to hundreds of thousands of monthly calls.
Every release risked breaking workflows already in production at Fortune 500 customers.
No way to validate behaviour under realistic production traffic at scale.
Enterprise buyers wanted objective numbers; the team had none to offer.
| Metric | Before Coval | With Coval |
|---|---|---|
| Test cycle time | 5 days of manual review | 4 hours, fully automated |
| Test infrastructure | Manual contractor evaluation | Automated simulation at production scale |
| Regression coverage | None | Full suite runs on every release |
| Load testing | Not available | 100 to 1,000+ concurrent calls validated |
| Pre-sales evidence | Vendor demos and promises | Objective performance numbers for every deal |
ServiceNow's VP approved the pilot in days, not months, because the math was simple: a seven-day Coval pilot against a six-month internal build. The team chose the seven days, and then kept choosing.
Coval went live before the conference demo deadline with automated test scenarios covering the workflows the team needed to ship. Synthetic conversation data eliminated the data-privacy review that would normally have slowed any vendor onboarding to a crawl.
What had been a week of manual contractor review compressed into half a working day of automated simulation. The team caught a phonetic substitution bug in staging that would have been a production incident at a Fortune 500 customer rollout.
Six months after the first pilot, the platform engineering team needed infrastructure validation at one hundred to one thousand concurrent calls before signing enterprise contracts. Coval scaled to meet it. Three business units — HR, CRM, and IT — now run their voice agents on the same shared testing foundation.
ServiceNow's voice AI team made GA launch on time, with a regression suite that catches issues before they reach production and load testing that scales with the deal. The pre-sales team uses Coval performance numbers in competitive deals where they used to have only vendor talking points.
"We had seven days to figure out how to test a voice agent at production scale. Coval was live and producing useful results before the deadline."
What started as a single team's emergency became standard infrastructure across three business units, with each new voice agent ServiceNow ships building on the same Coval foundation.
See what Coval can verify on your enterprise voice agent.
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