Building a conversational AI that performs flawlessly across real-world conditions requires more than spot checks or ad-hoc QA. It takes a complete, end-to-end testing suite; one that covers every stage from component accuracy to live monitoring - like Cekura.
A Unified Testing Environment for Voice & Chat AI
Cekura’s platform brings together simulation, evaluation, and monitoring into one continuous loop. Teams can test, validate, and benchmark conversational agents, whether powered by GPT-4o, Gemini, or custom LLMs, under realistic conditions before and after deployment.
| Layer | What It Covers | Example |
|---|---|---|
| Component Testing | Validate intent recognition, entity extraction, and response logic independently. | “Book me a table at 7” → confirm time and number of guests are both extracted correctly. |
| Integration Testing | Verify coordination across NLU, dialogue manager, and backend APIs. | Booking API fails → bot gracefully offers alternative time. |
| End-to-End Scenarios | Run full conversation flows including edge, off-script, and user-interrupt cases. | User interrupts mid-flow → agent recovers and continues correctly. |
| Regression & Version Control | Detect breaks after prompt or model updates. Cekura lets you replay production calls against new versions automatically. | Compare GPT-4o vs GPT-5 responses on identical scenarios. |
| Performance & Load | Stress-test under concurrent calls, degraded networks, and delayed APIs. | Measure P50/P90 latency and failure rate across 100 parallel calls. |
| Quality, Safety & Compliance | Evaluate hallucinations, bias, factual grounding, and privacy adherence. | Ask for sensitive info → ensure refusal per policy. |
| Monitoring & Drift Detection | Analyze production calls for regressions, emerging intents, or new failure patterns. | Automatic “instruction-following” metric flags unseen issues in live data. |
Built for Complete Coverage
Cekura’s scenario generator auto-creates test cases from your agent’s prompt, JSON, or knowledge base - no manual scripting required.
You can simulate varied personalities (e.g. “Interrupter,” “Pauser,” “Non-native accent”), inject noise and latency, and even define custom metrics using Python or your own LLM-as-judge evaluators.
Metrics That Matter
Cekura standardizes conversational testing with quantitative depth across every run:
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Speech Quality: Talk ratio, clarity, pronunciation, tone.
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Conversational Flow: Latency, interruptions, silence failures, termination behavior.
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AI Accuracy: Instruction-follow, relevancy, hallucination, tool-call success.
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User Experience: CSAT, sentiment, repetition rate.
Continuous Validation & Analytics
Each test run generates detailed dashboards and charts with turn-level timestamps, P50/P90 latency curves, and metric-wise Slack alerts.
Cekura also maintains a baseline suite for CI/CD pipelines, automatically re-evaluating after every prompt, model, or infra change.
A/B testing modules let teams compare agents side-by-side, tracking accuracy, response speed, and conversational quality over time.
Real Impact in Production
Companies like Quo use Cekura to accelerate releases and maintain reliability across updates: transforming QA from a manual checkpoint into a scalable automation loop.
With integrations for ElevenLabs, Bland, Vapi, Retell, and Pipecat, Cekura plugs directly into your stack to test at scale through text, voice, or SMS.
In short
Cekura’s complete conversational AI testing suite gives teams the power to simulate, evaluate, and monitor every aspect of conversational performance, before and after deployment, so your agents stay accurate, compliant, and ready for production.