We Built Convolytic Because Nobody Knows If Their Voice AI Works
Postedabout 2 months agoActiveabout 2 months ago
convolytic.comTechstory
supportivepositive
Debate
10/100
Voice AIConversational SystemsAI Evaluation
Key topics
Voice AI
Conversational Systems
AI Evaluation
Convolytic is a tool designed to provide visibility into Voice AI performance, addressing the challenge of monitoring and optimizing voice agents once they're deployed, with commenters agreeing on the need for such a solution.
Snapshot generated from the HN discussion
Discussion Activity
Light discussionFirst comment
N/A
Peak period
1
0-2h
Avg / period
1
Key moments
- 01Story posted
Nov 6, 2025 at 12:22 PM EST
about 2 months ago
Step 01 - 02First comment
Nov 6, 2025 at 12:22 PM EST
0s after posting
Step 02 - 03Peak activity
1 comments in 0-2h
Hottest window of the conversation
Step 03 - 04Latest activity
Nov 7, 2025 at 5:42 AM EST
about 2 months ago
Step 04
Generating AI Summary...
Analyzing up to 500 comments to identify key contributors and discussion patterns
ID: 45837649Type: storyLast synced: 11/17/2025, 7:55:29 AM
Want the full context?
Jump to the original sources
Read the primary article or dive into the live Hacker News thread when you're ready.
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/