Voice AI Testing: Validate Every Voice Agent Before It Talks to a User
Deploy autonomous AI evaluators against your voice AI agents. Simulate 200+ voice profiles, 50+ accents, and 15 real-world noise conditions to catch failures before real callers do.
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End-to-End Voice AI Testing
AI-native evaluators that generate voice scenarios, simulate real callers, and score voice AI quality across 9 metrics.
Score Every Voice AI Response
Every spoken exchange is graded across 9 quality metrics from generated WAV audio, the same evaluation depth as text, delivered over voice: hallucination, bias, context retention, completeness, and conversation flow.
9 Voice Quality Metrics
Bias, hallucination, completeness, context awareness, response quality, conversation flow, tone consistency, and more, scored on every spoken turn.
WAV Audio Interactions
Voice AI agents are evaluated through generated WAV audio conversations rather than static transcripts, mirroring how a real caller experiences the agent.
Go-Live Verdict
Every evaluation closes with a Green, Yellow, or Red production-readiness call across 4 weighted quality dimensions.
Inside Voice AI Testing on TestMu AI
See how TestMu AI scores conversation quality, simulates real acoustic conditions, covers scenarios, and flags issues automatically.
9 QUALITY METRICS
Score Voice AI Conversations on 9 Dimensions
Every voice AI agent, from an in-app voice assistant to an IVR system, is scored on the same nine quality metrics used for chat, generated over WAV audio with configurable pass/fail thresholds.
Try for free- Bias, hallucination, and completeness checks on every spoken response
- Context awareness, response quality, and natural conversation flow
- Tone consistency and root-cause understanding across multi-turn calls
REAL-WORLD ACOUSTICS
Test Against the Voices Your Users Actually Have
Voice AI testing only means something if it reflects real callers. Simulate accents, speaking speed, and background noise before your agent ever meets a live audience.
Try for free- 200+ voice profiles across ages, genders, accents, and dialects
- 15 background-noise presets, from call-center floors to poor cellular signal
- Configurable speaking pace, interruption patterns, and response delay
SCENARIO COVERAGE
Cover Happy Paths, Edge Cases, and Adversarial Inputs
AI-generated scenarios pull from your own product docs so voice AI testing covers what your agent will actually be asked, not a generic checklist.
Try for free- 60-100+ scenarios generated from PRDs, Jira, or Confluence
- Automatic spread across happy paths, edge cases, and compliance checks
- Custom validation criteria layered on top of the standard 9 metrics
ISSUE DETECTION
Catch Audio and Flow Failures Before Users Do
Beyond a pass or fail label, see exactly why a voice AI response broke, then wire the same checks into CI/CD with the testmu-a2a-cli.
Start Testing Your Voice AI Agent- Automated tags for patchy audio, latency, hallucination, and loop failures
- Speech-to-text mismatch logging down to the exact word
- CI-native --format junit output for GitHub Actions, GitLab, and Jenkins
Complete Voice AI Testing Coverage
Evaluation Confidence Scales With Test Volume
Every voice AI quality score carries a confidence rating tied to how many evaluations produced it, so a thin sample never gets reported as a solid verdict.
9 Metrics Scored on Every Voice Interaction
Hallucination, bias, completeness, context awareness, response quality, conversation flow, tone consistency, root-cause understanding, and positive user outcome.
One Go-Live Verdict Across 4 Dimensions
Functional Completeness, Quality Standards, Risk Profile, and Operational Readiness, each weighted at 25%, roll up into a single production-readiness call.
Pass, Fail, or Partial for Every Scenario
Every voice AI test scenario is tracked as Pass, Fail, or Partial against your defined criteria, so you know exactly which conversations need attention.
Built for Every Layer of Voice AI QA
Project and Environment Management
Create voice AI test projects, manage environments, and configure variables with bulk scenario setup for multi-region voice deployments.
Persona Library and Voice Profiles
Test across 10 pre-built caller personas and 200+ voice profiles, from impatient callers to international speakers with diverse accents.
Custom Validation Rules
Define evidence-based pass/fail criteria per voice scenario, from required disclosures to escalation triggers, with High, Medium, and Low confidence tracking.
Secure Infrastructure
Run voice AI evaluations via HyperExecute with optional secure tunnels for agents behind corporate firewalls or in private VPC environments.
Scheduled Regression Testing
Automate voice AI regression runs on preset frequencies or custom cron schedules with IANA timezone support to catch quality drift early.
Observability and Reporting
Monitor voice AI quality across test runs with unified dashboards, exportable audit reports, and real-time metric trend analysis.
Success Stories of TestMu AI (Formerly LambdaTest)
50%
reduction in test execution time
“HyperExecute is a highly reliable test execution platform and has excellent customer support.”
Sagar Uday Kumar
Sr. Engineering Manager
Some Love from our Customers
As Best Egg expanded its product offerings and entered new markets, we knew our old testing infrastructure couldn’t keep up.
With support from Tenny Agustin, our Engineering Operations Lead, we modernized our approach with
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Best Egg
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Excited to Share My Learning Journey with Kane AI & Lambda Tool!
I'm pleased to announce that I've recently gained hands-on experience exploring Kane AI through the Lambda Tool and it’s been a fantastic journey of upskilling!
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Suryateja Goud
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See how is #Futureready to enable blazing-fast test orchestration seamlessly integrated with organizations' existing CI/CD platforms, using #Microsoft Azure.
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