Out-of-the-box 30+ predefined metrics for analysis on CX, accuracy, conversation and voice quality. Compile perfect LLM judges by annotating just ~20 conversations and auto-improve in Cekura labs. Real-time, segmented dashboards to identify trends in Conversational AI. Smart statistical alerts so that you get notified only when metrics shift from historical baselines. Automated system pings to catch silent production failures.
We are excited to launch Cekura Monitoring for Voice and Chat AI companies. Most monitoring tools tell you if your AI is up. Cekura tells you if it is behaving.
When we had first launched Cekura QA, we thought we had solved the problem for both testing and monitoring . But as our users scaled, we noticed a painful pattern: While pre-production QA was automated, teams were still spending dozens of hours manually listening to thousands of calls.
The two big blockers we saw were:
The Scaling Wall: Defining and optimizing custom metrics was taking too long, forcing teams back into manual spot-checks.
Production Blindspot: Standard LLM metrics misses the Customer Experience in Voice AI - things like agent tone and customer sentiment that actually defines customer success.
We have rebuilt the monitoring layer from the ground up to solve this. Cekura Monitoring turns that "wall of noisy logs" into actionable signals.
🚀 What’s New in Cekura Monitoring:
30+ Predefined Metric Suite: We track what actually breaks Voice and Chat agents across four critical categories:
Speech Quality: Voice clarity, pronunciation, and gibberish detection.
Conversational Flow: Silences, interruptions (barge-ins), and termination triggers.
Accuracy & Logic: Hallucinations, transcription accuracy, and relevancy.
Customer Experience: CSAT, Sentiment analysis, and drop-off points.
Metric Optimizer: Stop "vibes-based" prompt engineering. Define a metric (e.g., Successful User Authentication), tag 20 calls in our Labs interface, and our optimizer "compiles" a prompt that aligns with your specific feedback.
Statistical Intelligence: No more fixed, noisy thresholds. Our Alerting Engine learns your agent's baseline and only pings Slack when metrics shift 2σ from historical norms.
Automated Cron Jobs: Set up recurring health checks to simulate production conversations. Catch silent failures and regressions before your customers do.
Visual Dashboards: Real-time distribution charts for each metric. Views customized for each stakeholder
Who is this for?
Teams scaling Voice & Chat AI who are tired of listening to calls manually and need a way to prove their agents are actually working.
Sign up and try for free at cekura.ai or drop your questions below! We would love to hear how you’re currently handling Voice and Chat AI in production👇
Hi Product Hunt! 👋
We are excited to launch Cekura Monitoring for Voice and Chat AI companies. Most monitoring tools tell you if your AI is up. Cekura tells you if it is behaving.
When we had first launched Cekura QA, we thought we had solved the problem for both testing and monitoring . But as our users scaled, we noticed a painful pattern: While pre-production QA was automated, teams were still spending dozens of hours manually listening to thousands of calls.
The two big blockers we saw were:
The Scaling Wall: Defining and optimizing custom metrics was taking too long, forcing teams back into manual spot-checks.
Production Blindspot: Standard LLM metrics misses the Customer Experience in Voice AI - things like agent tone and customer sentiment that actually defines customer success.
We have rebuilt the monitoring layer from the ground up to solve this. Cekura Monitoring turns that "wall of noisy logs" into actionable signals.
🚀 What’s New in Cekura Monitoring:
30+ Predefined Metric Suite: We track what actually breaks Voice and Chat agents across four critical categories:
Speech Quality: Voice clarity, pronunciation, and gibberish detection.
Conversational Flow: Silences, interruptions (barge-ins), and termination triggers.
Accuracy & Logic: Hallucinations, transcription accuracy, and relevancy.
Customer Experience: CSAT, Sentiment analysis, and drop-off points.
Metric Optimizer: Stop "vibes-based" prompt engineering. Define a metric (e.g., Successful User Authentication), tag 20 calls in our Labs interface, and our optimizer "compiles" a prompt that aligns with your specific feedback.
Statistical Intelligence: No more fixed, noisy thresholds. Our Alerting Engine learns your agent's baseline and only pings Slack when metrics shift 2σ from historical norms.
Automated Cron Jobs: Set up recurring health checks to simulate production conversations. Catch silent failures and regressions before your customers do.
Visual Dashboards: Real-time distribution charts for each metric. Views customized for each stakeholder
Who is this for?
Teams scaling Voice & Chat AI who are tired of listening to calls manually and need a way to prove their agents are actually working.
Sign up and try for free at cekura.ai or drop your questions below! We would love to hear how you’re currently handling Voice and Chat AI in production👇