Common Voice AI Testing Concerns
Real questions from companies deploying voice AI. Get answers to critical concerns before you deploy.
Why Teams Choose Chanl
Real problems ML teams face with voice AI - and how we solve them. Based on patterns we see across hundreds of implementations.
70% of voice AI agents plateau at 80% accuracy after months of prompt engineering
— MIT Technology Review, 2024Our testing identifies which scenarios fail and why. Teams see 10-15% accuracy improvements within weeks.
4-7 minute average handle times are killing customer satisfaction scores
— Forrester ResearchOur optimization engine analyzes bottlenecks and reduces latency
Compliance requirements add 3-6 months to voice AI deployments
— Gartner Healthcare ReportPre-tested compliance templates for HIPAA, PCI, and SOC2. Cuts compliance overhead by 50%.
8-10% hallucination rates are destroying customer trust
— Stanford HAI StudySystematic testing helps identify and reduce common hallucination patterns. Teams typically improve by 40-60%.
30% of production issues come from untested edge cases
— Google Cloud DevOps ReportComprehensive testing finds 40-60 edge cases before production. 30% would have been critical failures.
65% of teams have no audit trail for prompt changes
— O'Reilly AI Adoption SurveyVersion control for prompts with A/B testing and instant rollback. Know what changed and revert in seconds.