Cekura

Product Introduction

1. Definition: Cekura is an enterprise-grade end-to-end testing and observability platform specifically engineered for Conversational AI. It functions as a specialized quality engineering layer for LLM-powered voice and chat agents, providing automated simulations, real-time production monitoring, and automated evaluation metrics.

2. Core Value Proposition: Cekura enables AI engineering teams to launch reliable voice agents in minutes rather than weeks by automating the testing of thousands of conversational scenarios. It eliminates the unpredictability of LLM deployments through "LLM-as-a-judge" technology, statistical alerting for performance shifts, and automated detection of silent production failures. By providing 30+ predefined metrics, Cekura ensures that Conversational AI delivers a seamless user experience across diverse personas and edge cases.

Main Features

1. Automated End-to-End Simulation & Parallel Calling: Cekura utilizes a library of thousands of pre-configured scenarios to stress-test AI agents before deployment. The platform supports parallel calling, allowing developers to simulate high-volume traffic and diverse user behaviors simultaneously. This includes testing for interruptions, off-script interactions, and complex user flows such as appointment cancellations or reschedules.

2. Cekura Labs & Custom LLM Judges: This feature allows teams to compile high-accuracy LLM judges by annotating a small sample size of approximately 20 conversations. Cekura Labs then uses these samples to auto-improve evaluation accuracy. The system analyzes specific dimensions including empathy, responsiveness, hallucinations, and compliance (e.g., ensuring legal disclaimers are stated).

3. Multi-Persona Voice Testing: Cekura provides diverse AI personas with various accents and temperaments (e.g., Female American/Professional, Male British/Professional, Female Indian/Pleasant, Male German/Angry) to test how voice agents handle different linguistic nuances and emotional states. This is critical for global deployments where accent recognition and emotional intelligence are required.

4. Real-Time Observability & Statistical Alerting: The platform offers segmented dashboards that visualize trends in Conversational AI performance. Unlike standard monitoring, Cekura employs smart statistical alerts that notify engineers only when metrics deviate significantly from historical baselines, preventing alert fatigue while catching critical prompt regressions or tool-call failures.

5. Deep Integration Ecosystem: Cekura integrates natively with leading Voice AI and infrastructure providers, including Vapi, Retell AI, Bland, Synthflow, LiveKit, Pipecat, ElevenLabs, and Cisco. These integrations allow for automated system pings and seamless data ingestion from both inbound and outbound telephony workflows.

Problems Solved

1. Prompt Regression and Flow Breakage: When developers update a prompt to improve one area (e.g., greeting), it often inadvertently breaks another core flow (e.g., appointment booking). Cekura identifies these regressions instantly by replaying known trouble spots and running automated regression suites.

2. Silent Production Failures: AI agents may fail without crashing—such as by hallucinating information or skipping mandatory compliance checks. Cekura’s automated pings and instruction-following monitors catch these "silent" errors that traditional logging misses.

3. High Manual QA Costs: Manually listening to recordings of voice AI calls is unscalable. Cekura replaces manual QA with automated evaluation metrics, reducing the time required to assess agent quality from hours to minutes.

4. Target Audience:

• AI Product Managers: Who need to ensure brand-safe and compliant customer interactions.
• Conversational AI Engineers: Who require robust CI/CD pipelines for prompt engineering and LLM updates.
• CX (Customer Experience) Leaders: Who monitor the empathy and responsiveness of automated support agents.
• Forward Development Engineers (FDEs): Tasked with scaling AI employees in specialized sectors like Healthcare, Legal, and Debt Collection.

5. Use Cases:

• Healthcare Onboarding: Ensuring precision and privacy compliance during patient intake (e.g., Twin Health).
• Debt Collection: Testing for strict adherence to regulatory scripts and handling angry or resistant debtors.
• AI Appointment Booking: Verifying that the agent can handle interruptions and complex rescheduling logic without losing context.
• Outbound Sales: Evaluating the conversion effectiveness and persistence of sales agents.

Unique Advantages

1. Hyper-Specific Voice Optimization: Unlike generic LLM monitoring tools, Cekura is built specifically for the "Voice" in Voice AI. It accounts for latency, interruptibility, and audio quality metrics that are unique to telephony and real-time streaming.

2. Efficiency in Annotation: While other platforms require thousands of labeled data points to train an evaluation model, Cekura’s "Few-Shot" approach allows teams to build "Perfect LLM Judges" with minimal data input (~20 conversations), significantly speeding up the development lifecycle.

3. Closed-Loop Improvement: The platform doesn't just monitor; it "auto-improves" via Cekura Labs. It identifies where the agent failed and provides the data necessary to refine the underlying prompts or tool-calling logic.

Frequently Asked Questions (FAQ)

1. How does Cekura detect hallucinations in Voice AI agents? Cekura utilizes specialized LLM judges trained to cross-reference agent responses against a provided knowledge base or "ground truth" scenarios. By analyzing the transcript and metadata in real-time, the platform can flag instances where the agent provides information not contained in its instructions or makes incorrect tool calls.

2. Can Cekura test how my AI agent handles interruptions? Yes. One of Cekura’s core features is the "Impatient User" simulation. It tests the agent's ability to handle user barge-in, maintain state after an interruption, and return to the primary task flow without repeating itself or crashing.

3. Which Voice AI platforms does Cekura support? Cekura features native, out-of-the-box integrations with major platforms including Retell AI, Vapi, Bland, Synthflow, and ElevenLabs. It also supports infrastructure-level integrations with Cisco, LiveKit, and Pipecat, making it compatible with both proprietary and open-source voice stacks.

4. What are "Statistical Alerts" and how do they differ from standard notifications? Standard notifications trigger on every error, which can lead to noise. Cekura’s statistical alerts use historical baseline data to understand the "normal" variance in your agent's performance. You are only notified when there is a statistically significant shift in metrics—such as a sudden 10% drop in empathy scores or a spike in tool-call latency—indicating a genuine systemic issue.