Voice AI systems aren’t just judged by how well they talk but also by how well they hold up when reality hits.
That means noisy lines, overlapping speech, delayed APIs, accent diversity, and thousands of concurrent calls all competing for resources.
Cekura is the platform built to push your voice agents through exactly those conditions before customers ever do.
1. Load and Scalability Testing
Cekura simulates high-volume call environments to reveal infrastructure limits long before production does. Teams can run thousands of concurrent voice simulations, gradually increase load, and track metrics like mean, P50, and P95 latency to spot bottlenecks.
Failures, timeouts, speech drop-offs, and server stalls, are logged and replayable, giving developers a reproducible path to fix and re-verify performance under stress.
2. Real-World Audio Variation
Cekura’s personality engine introduces dozens of realistic speech profiles: fast talkers, interrupters, hesitant speakers, and heavy-accented voices. So your agent faces the kind of variability it will meet in real calls.
Background noise, poor mic quality, and multilingual accents can all be layered into tests.
The latest updates also support custom background noise uploads and cloned voice IDs via Cartesia integration, allowing hyper-specific simulation of your user base.
3. Dialogue and Intent Resilience
Beyond ASR accuracy, Cekura stresses conversational endurance. It throws interruptions, incomplete phrases, and adversarial phrasing at the agent to see whether it recovers context, stays on task, and completes the goal.
Teams can include “sad path,” “toxic,” or “bias” scenarios to measure how the voice model handles hostility, silence, or refusal conditions, critical for robustness in sensitive use cases like healthcare and finance.
4. Full-Pipeline Simulation
Cekura connects directly to your telephony and voice stack - whether you use Vapi, Retell, Bland, ElevenLabs, Pipecat, or LiveKit - calling your real agent endpoints rather than mock APIs.
Each test walks through the entire speech-to-intent-to-tool-call pipeline, validating latency, transcription accuracy, and backend execution.
Confido Health used this approach to safely migrate its entire voice infrastructure while maintaining zero regression across workflows.
5. Deep Metrics and Failure Analysis
Every simulation produces granular voice and behavioral metrics:
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Speech Quality: WPM, pitch, clarity, pronunciation
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Flow Metrics: latency, interruptions, silences, termination handling
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Accuracy Metrics: instruction following, response consistency, hallucination, tool-call success
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Experience Metrics: CSAT, sentiment, voice tone
Failures are visualized with timestamps and replayed for diagnosis, and teams can compare baseline vs. new versions side-by-side to spot regressions automatically.
6. Automation, CI/CD, and Alerts
Cekura’s API and CI/CD hooks let you schedule nightly regression suites or trigger stress tests whenever a new prompt, model, or infrastructure change is pushed.
Metric-wise Slack and email alerts flag spikes in latency or drop rates as they occur, ensuring that production reliability is continuously monitored, not just pre-launch.
Why Teams Choose Cekura
Voice AI builders from fast-moving startups like Quo to regulated healthcare providers like Confido Health use Cekura to validate that every agent is scalable, stable, and trustworthy under real-world pressure.
Cekura’s platform turns stress testing from a one-off chore into a continuous safeguard - one that measures how your AI performs when it matters most.