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👇
We are currently building AI support for a large corporation. In such projects, there is an issue with recognizing smaller languages (for example, Swedish). Can you analyze only English, or other languages as well?
Congratulations on your launch @kabra_sidhant. Trying to map this mentally— Is Vocera closer to:
testing (like Playwright)
observability (like Datadog)
or eval frameworks (like DeepEval)?
Or is it a new category altogether?
AI eval tools are exploding right now, but most stop at surface-level metrics.
The hard part is tying these signals to real business outcomes (conversion, CSAT, retention).
How are you bridging that gap vs just reporting latency / sentiment?
Amazing product! Congrats team :) What are some of the voice only direct evals that the platform can perform?
We've been using Cekura for our voice AI testing and observability for the last year and the product is the best in the market. The team absolutely cooks!
this is cool. the 30 predefined metrics thing is smart cause most ppl building voice agents dont even know what to measure at first. nice that you dont have to start from scratch
🚀 I’m so proud of the work we’ve done on Cekura Monitoring. I personally worked on the Smart Metric Alerting engine, which saves Voice and Chat AI teams from scrolling through thousands of calls. Now, you only get a ping when something actually feels off.
The best part? The customization. It allows our users to tune out the noise and focus purely on the performance metrics that define their success. It’s a total game-changer for anyone scaling AI agents.
Really helpful feature.
Congrats team!!! Do you support real-time streaming analysis or is it batch processed right now?
Would love an API-first version of this for deeper integration into internal tooling.
How do you handle false positives in sentiment or hallucination detection?
This feels like Datadog but for AI behavior instead of infrastructure. That's a good positioning. Congratulations!!
This is such a natural evolution from QA to monitoring. Congrats on shipping.
Congrats. Have you considered integrations with tools like HubSpot or Zendesk for closing the loop on CX insights?
When Cekura flags an issue in production, what does fixing it actually look like in practice? Do teams usually retrain models, tweak prompts, or handle it more on a case‑by‑case basis?
This is great- especially out of the box metrics. Which ones do people use most in prod?
This is a massive launch for such a critical problem in conversational agents today. Curious, what are the most important metrics tracked by customers in the healthcare space?
About Cekura on Product Hunt
“Observe and analyze your voice and chat AI agents”
Cekura launched on Product Hunt on March 24th, 2026 and earned 445 upvotes and 105 comments, earning #2 Product of the Day. 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.
Cekura was featured in SaaS (41.5k followers), Developer Tools (511k followers) and Audio (2k followers) on Product Hunt. Together, these topics include over 108.9k products, making this a competitive space to launch in.
Who hunted Cekura?
Cekura was hunted by Garry Tan. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Cekura stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Garry Tan
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👇