Customer stories
AI-native teams use Moda to close the loop.
Moda helps teams move from anecdotal transcript review to a shared operating rhythm for agent quality.
Learning signal
Production conversations
Boardy
Used conversation clusters to reduce repeated frustration loops and prioritize high value intents.
Octolane
Turned tool failure patterns into a weekly learning workflow for agent engineers and support.
Codewisp
Expanded coverage by finding new tasks users expected the copilot to handle in production.
3.1%
frustration after focused fixes
4h
time to learn from new failures
94%
known intent coverage after review
What teams ship
Sharper product priorities
Customer intent clusters reveal the workflows users actually want from an AI product.
Faster agent fixes
Traceable failure patterns give teams the confidence to fix prompts, tools, and workflows quickly.
Boardy
Used conversation clusters to reduce repeated frustration loops and prioritize high value intents.
Octolane
Turned tool failure patterns into a weekly learning workflow for agent engineers and support.
Codewisp
Expanded coverage by finding new tasks users expected the copilot to handle in production.
AI support teams
Connect escalations, unresolved conversations, and agent behavior into a shared improvement backlog.
Want your own customer story?
Bring us a representative sample of production conversations and we will show the learning patterns inside them.