Over the past few months, I’ve been building and deploying real Voice AI agents for businesses — everything from booking calls to customer service to healthcare intake.
What I learned was kind of shocking: once you ship a voice agent, you basically fly blind.
You can’t easily tell why calls fail.
You can’t measure whether a prompt, voice, or model change improves performance.
There’s no Mixpanel or Datadog for conversational AI — just logs and vibes.
We built Convolytic to fix that.
It’s an analytics and optimization platform for voice and chat agents — a way to actually measure, A/B test, and improve performance in production.
It answers questions like:
Did latency or tone cause this call to drop?
Which LLM or TTS stack performs better for this workflow?
How much revenue are we losing because the AI didn’t follow up or upsell?
In one real estate pilot, we found agents booked one showing but never suggested a second — a missed 40% revenue opportunity hiding behind “successful” calls.
We started out just needing observability for our own agents, but other teams building with Vapi, Retell, Synthflow, and custom STT/TTS stacks asked for access, so now we’re opening it up.
If you’re building or running voice/chat agents, I’d love feedback — what metrics would you track if you could?
We’re early, but the core platform already supports multi-model benchmarking, A/B testing, and call-level analytics.
Over the past few months, I’ve been building and deploying real Voice AI agents for businesses — everything from booking calls to customer service to healthcare intake.
What I learned was kind of shocking: once you ship a voice agent, you basically fly blind.
You can’t easily tell why calls fail.
You can’t measure whether a prompt, voice, or model change improves performance.
There’s no Mixpanel or Datadog for conversational AI — just logs and vibes.
We built Convolytic to fix that. It’s an analytics and optimization platform for voice and chat agents — a way to actually measure, A/B test, and improve performance in production.
It answers questions like:
Did latency or tone cause this call to drop?
Which LLM or TTS stack performs better for this workflow?
How much revenue are we losing because the AI didn’t follow up or upsell?
In one real estate pilot, we found agents booked one showing but never suggested a second — a missed 40% revenue opportunity hiding behind “successful” calls.
We started out just needing observability for our own agents, but other teams building with Vapi, Retell, Synthflow, and custom STT/TTS stacks asked for access, so now we’re opening it up.
If you’re building or running voice/chat agents, I’d love feedback — what metrics would you track if you could? We’re early, but the core platform already supports multi-model benchmarking, A/B testing, and call-level analytics.
https://www.convolytic.com/