LLM-as-a-judge based monitoring is not enough for Voice AI

Most teams scaling Voice AI think they can monitor quality with a simple LLM prompt. They are wrong.

An LLM can’t hear a "crunchy" voice line, it can’t accurately measure a 500ms "barge-in," and it struggles with the nuances of true conversational flow.

When we built Cekura Monitoring, we realized we had to go beyond the LLM. We combined Heuristic and Statistical models with our Metric Optimizer to solve the "Scaling Wall."

What we’re actually tracking:

• Signal-Level Heuristics: We detect what LLMs miss - voice clarity, pronunciation, and gibberish.

• Conversational Logic: Measuring silences, interruptions, and termination triggers using precise statistical baselines.

• Statistical Intelligence: No more noisy, fixed thresholds. Our engine learns your agent's baseline and only pings Slack when metrics shift 2σ from historical norms.

• The Metric Optimizer: Define a goal, tag 20 calls with your feedback, and the system "compiles" the logic to align with your specific standards.

The Goal: Turn a "wall of noisy logs" into actionable signals without your team spending 20 hours a week manually listening to calls.

We are going live on Product Hunt today! 🚀https://www.producthunt.com/products/vocera?launch=cekura-2

Would love your thoughts and feedback!!