How are you using Fin AI to improve customer support quality? | Community
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How are you using Fin AI to improve customer support quality?

  • August 7, 2026
  • 1 reply
  • 108 views
michelvilsen

Hi everyone,

I'm currently exploring different ways to improve customer support workflows using AI. I was wondering how other teams are measuring the quality of responses generated by Fin AI.

A few questions I have:

  • What metrics do you monitor the most?
  • Have you noticed improvements in first-response resolution?
  • Do you use Fin mainly for FAQs, product support, or live conversations?
  • Any best practices for training AI with a growing knowledge base?

I'd love to hear how your team approaches this and what has worked well in real-world use cases.

Thanks!

 

Best answer by Adam Warden

Hi Michel! 

 

I’ve helped quite a few businesses set up and optimize Fin. Typically, the main metrics I tend to keep an eye on are Fin’s resolution rates and Fin’s CSAT scores. Resolution rate to see if it’s actually fixing customer issues and CSAT to see how well customers are receiving it. 

 

Once these metrics are up and running, I focus on what’s working and even more so on what’s not working to see areas for improvement. This usually entails going through unsatisfactory and escalated conversations to see what Fin could have done better and where I can automate further.

 

Fin is really great at what it does so in terms of implementation I try to add it where ever possible as long as it’s improving support quality. By improving quality I specifically mean quicker response times while maintaining high satisfaction.

 

As for training AI with a growing knowledge base, it’s important to make sure all the information is well formatted and accurate. Batch testing and guidance are great tools here to ensure accuracy.

 

Hope this helps!

1 reply

Adam Warden
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  • Adam Warden
  • Innovator ✨
  • Answer
  • August 13, 2026

Hi Michel! 

 

I’ve helped quite a few businesses set up and optimize Fin. Typically, the main metrics I tend to keep an eye on are Fin’s resolution rates and Fin’s CSAT scores. Resolution rate to see if it’s actually fixing customer issues and CSAT to see how well customers are receiving it. 

 

Once these metrics are up and running, I focus on what’s working and even more so on what’s not working to see areas for improvement. This usually entails going through unsatisfactory and escalated conversations to see what Fin could have done better and where I can automate further.

 

Fin is really great at what it does so in terms of implementation I try to add it where ever possible as long as it’s improving support quality. By improving quality I specifically mean quicker response times while maintaining high satisfaction.

 

As for training AI with a growing knowledge base, it’s important to make sure all the information is well formatted and accurate. Batch testing and guidance are great tools here to ensure accuracy.

 

Hope this helps!

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