Most teams ship voice AI agents and hope for the best. ContextQA validates accents, interruptions, turn-taking, latency, hallucinations, task completion, and knowledge-base accuracy — all in one run, before go-live.
AI voice agent testing validates a voice agent the way real callers experience it — on real calls, with realistic personas. ContextQA checks accents, interruptions, turn-taking, latency, hallucinations, task completion, and knowledge-base accuracy, scoring every call with AI and deterministic judges before your agent goes live.
Text agents already clear the bar. The same agent, on a real call, drops sharply — and the gap widens under real-world conditions.
Source: Sierra AI, τ-voice benchmark, 2026 · 278 grounded tasks across retail, airline, and telecom
An agent with identical prompts and tools behaves differently on a call. Tone, interruptions, and accents surface breaks that text testing never catches.
A voice agent can hold a polite, natural conversation while quietly failing the underlying task. Each turn sounds fine — the account never gets updated.
Non-standard accents, noisy environments, spotty connections. The callers most affected by regressions are the ones a quiet-room demo never represents.
From connecting the agent to the final executive report — the complete run in one video.
Amazon Connect, WebRTC, or a plain phone number — no SDK or code access required.
Drop in your agent brief and knowledge base so tests reflect what the agent should know.
Personas and use cases are generated from your brief, then test cases for each — with expected outputs and follow-ups.
Real calls are placed and scored by AI and deterministic judges against your pass threshold.
Full call transcripts plus an executive summary and a developer deep-dive.
Voice quality and functional behavior, validated in the same run.
Does it sound right, to every caller?
Does it do the right thing, every time?
Subjective quality and hard proof — you get a confidence score backed by evidence, not a vibe.
AI judges score the qualities only a listener can assess, against criteria you configure.
Hard checks that pass or fail — no judgment calls, just verifiable facts.
Point ContextQA at your Connect instance and start placing test calls.
Test browser-based voice agents over a direct WebRTC connection.
Dial the agent like a real customer — landline or mobile.
Direct trunk into your telephony stack for enterprise contact-center testing at scale.
Works across audio-native models, ASR + LLM + TTS pipelines, and contact-center platforms — vendor-neutral by design.
Runtime infrastructure improves the call while it happens. ContextQA answers the question that comes before: is this agent ready to take real calls at all?
Runs while the call is happening, shaping the experience in real time.
Validates the agent the way callers will experience it, before it ever takes a real call.
One layer makes live calls better; the other proves the agent is ready for them. Teams run both.
Which test cases passed, which failed, whether the agent is ready for launch, and the top failure modes — with actionable insights instead of raw metrics.
Every test case with expected vs. actual outcome, score, and the reasoning behind each result — plus full call transcripts you can replay. Pair it with root-cause analysis to fix issues fast.
Testing chat and tool-calling agents too? See AI agent testing for the full picture.
Security and deployment flexibility for teams validating voice agents that touch real customer data.
Certified. Security and integration documentation available for review.
SaaS or fully self-hosted inside your own cloud account, under your IAM.
Per-project data separation, built for teams validating multiple agents.
Whether it's an insurance support agent, a customer-service bot, or any platform-built voice AI — ContextQA validates it before real users do.
What you test
Catch hallucinations and drift before users do
SAP, Oracle, Workday & custom ERP
Browser automation across every engine
Native iOS and Android coverage
REST and GraphQL validation
CRM workflow automation
Enterprise application coverage
See what a code change breaks before merge
How you test
Plan, track, and manage every test
Tests repair themselves as code shifts
Pinpoint why a test broke, instantly
Catch unintended UI change
Load and stress at scale
Always-on across every release
Platform & AI
Parallel cloud grid, every browser and device
One prompt drives 50 testing tools
Test assets and code export
Real user intelligence and analytics
Connect Jenkins, Jira, CI/CD
IBM, Coforge, Red Hat
Analyze End-To-End Tests
Platform & AI
Not three tools.
