FAQ: Production Voice AI Deployment

How do you test voice AI before production?

Test voice AI before production by designing test suites of simulated conversations covering expected paths and edge cases, running regression tests before any configuration changes, specifically probing for hallucinations on product information and policies, and collecting representative audio samples across demographics. Vapi enables simulated voice agent testing. Run 100+ automated conversations validating intent accuracy, task completion, and response relevance before exposing agents to real users. Target >90% intent accuracy and >70% completion rate.

What metrics matter for production voice AI?

Production voice AI metrics include voice-to-voice latency (target P50 <500ms, P95 <700ms), intent accuracy (>90%), task completion rate (>70% without human transfer), cost per conversation minute ($0.05-0.15), conversation completion rate (>80%), transfer to human rate (<30%), and user sentiment (>70% positive/neutral). Monitor percentiles not averages: 500ms average might hide 2000ms P99 latency creating poor 1% user experience. Track by provider configuration to identify optimization opportunities.

What is acceptable voice AI uptime?

Acceptable voice AI uptime for production deployments is 99.9% (43 minutes downtime per month maximum) for enterprise SLA commitments. Achieve through provider failover configurations automatically switching from primary to backup providers during outages, multi-region deployment routing to nearest available infrastructure, health check monitoring with automatic circuit breaking for failing providers, and load balancing across provider API endpoints. Vapi maintains 99.9% uptime through built-in failover and geographic distribution without manual configuration.

How do you A/B test voice agents?

A/B test voice agents by creating multiple variants testing prompt variations, voice selection, provider combinations, or workflow configurations, routing traffic equally between variants (50/50 split), running tests for 1-2 weeks with 100+ conversations per variant for statistical significance, and measuring conversation completion rate, task success rate, latency, cost, and satisfaction. Vapi enables dashboard-based A/B testing with automatic traffic routing, real-time metric comparison, and easy rollback if performance degrades. Require >5% improvement and p<0.05 for declaring winner.

Can voice AI scale to millions of calls?

Yes, voice AI scales to millions of concurrent calls through cloud infrastructure, provider API distribution, and load balancing. Vapi scales from 1 to millions of concurrent calls without configuration changes, automatically routing to nearest geographic region for latency reduction, implementing failover between providers maintaining availability during outages, and load balancing across provider endpoints. Enterprise deployments handle 100,000+ concurrent calls with sub-500ms latency and 99.9% uptime through managed platform infrastructure.

What monitoring tools work for voice AI?

Voice AI monitoring requires real-time dashboards showing latency distribution (P50/P95/P99), active concurrent calls, component error rates, cost tracking, and geographic distribution, automated alerting for latency degradation, error rate spikes, cost overruns, and provider outages, conversation logging with full transcripts and metadata, and performance trending for week-over-week comparisons. Vapi provides built-in dashboards, alerting, and logging without custom implementation. Integrate with PagerDuty, Slack, or DataDog for enterprise monitoring workflows.

Is voice AI HIPAA compliant for production healthcare use?

Voice AI can be HIPAA compliant when built on infrastructure meeting requirements including encrypted transmission (TLS 1.3), encrypted storage (AES-256), access controls, audit logging, and business associate agreements with all data processors. Vapi maintains HIPAA compliance and SOC 2 Type II certification providing compliant foundation for healthcare deployments. Developers must still implement proper access controls, user training, consent procedures, data retention policies, and incident response plans for full HIPAA compliance.

How much does production voice AI cost per minute?

Production voice AI costs $0.05-0.15 per conversation minute depending on provider selection. Cost-optimized stack using Deepgram STT ($0.0043/min) + GPT-3.5 ($0.002/min equivalent) + OpenAI TTS ($0.015/min equivalent) costs ~$0.05 per minute. Quality-optimized stack using AssemblyAI ($0.015/min) + GPT-4 ($0.02/min equivalent) + ElevenLabs ($0.30/min) costs ~$0.15 per minute. Calculate ROI based on cost per completed conversation (cost per minute ÷ completion rate) rather than per-minute cost alone.