Teams building conversational AI need a testing system that keeps up with rapid iteration. A single prompt change, model upgrade, or routing tweak can silently break core workflows. Cekura provides an end-to-end testing framework that gives teams confidence in every release - across every channel, model, and environment.
Cekura maps full conversational flows with multi-turn dialogs, branching logic, interruptions, retries, fallbacks, memory checks, and entity or slot validation. It tests agents across chat, SMS, WhatsApp, web widgets, and voice channels.
Voice agents benefit from detailed ASR/TTS evaluation, clarity checks, timing analysis, and more through Cekura’s rich library of predefined and custom metrics.
Cekura is platform-agnostic. Whether an agent runs on Dialogflow, Lex, Rasa, Botpress, an LLM orchestrator, or custom pipelines - and whether voice is delivered through Retell, Vapi, Pipecat, LiveKit, ElevenLabs, or telephony - Cekura integrates seamlessly.
Teams can validate tool calls, API behavior, downstream effects, and failure handling, even under interruptions, different accents, noisy environments, or challenging user personas.
Testing ranges from functional checks to regression suites, load and stress scenarios, latency evaluation, hallucination detection, and safety validations. Cekura’s scenario engine also supports adversarial inputs, jailbreak attempts, bias or toxicity probes, and controlled persona shifts using over 50 specialized personalities, including interrupters, pausers, slang-heavy speakers, and non-native accents.
CI/CD-Ready by Design
Cekura fits directly into modern engineering workflows. The entire platform is API-driven, allowing teams to trigger test suites from GitHub Actions, GitLab, Jenkins, Azure DevOps, or any automation tool. Every update, whether a prompt adjustment, model swap, or infrastructure migration, can automatically run through a predefined regression suite. Baselines can be locked, versioned, and compared side-by-side to ensure nothing breaks during iteration.
Environment support spans dev, staging, and production. Teams can retest older versions, set automated promotion gates, and enforce thresholds for accuracy, pass rates, latency, or safety. Cekura can schedule nightly replays or trigger evaluations when new models become available.
For scale testing, Cekura runs large batches in parallel, uncovering concurrency issues, timeout behavior, network degradation, or infrastructure bottlenecks. Stress-mode testing helps teams predict reliability before real traffic arrives.
Deep Reporting and Debugging
Each run produces readable and machine-ready results, including JSON and JUnit XML. Step-level transcripts, timestamped failures, and conversation diffs show exactly where behavior diverged from expectations. Teams can replay production calls, simulate them under controlled conditions, and confirm that identified issues are fixed before merging changes.
Cekura tracks intent accuracy, entity correctness, end-to-end success rates, latency, drift, tool-call reliability, sentiment, and user experience. Trend-based alerts automatically surface anomalies and regressions. Dashboards visualize patterns over time and highlight failure points clearly .
Secure, Stable, and Scalable
Cekura includes dataset versioning, PII masking, and automated redaction for audio and transcripts. Access control, secret management, and compliance support (including SOC 2, ISO, and HIPAA) ensure testing can be done safely — even for sensitive workloads. Teams can mock backend services or test with real APIs to validate true end-to-end behavior.
Organizations - like Confido Health and Quo - use Cekura to validate complex workflows, verify backend integrations, de-risk infrastructure migrations, and compare models like GPT-4o vs GPT-5 under identical scenarios. This makes it possible to ship updates with confidence and maintain reliability as systems grow