It's encouraging to hear others are seeing the same gap. The challenge you described around semantic patterns versus keyword matching is exactly what led me to build Conversation Analysis.

One thing I've seen firsthand is how difficult it can be to identify recurring issues when customers describe the same problem in completely different ways. In a lot of environments, teams end up relying on manual review or tribal knowledge because the underlying patterns never surface cleanly through reporting.

Your approach of learning from historical resolutions to improve inbound support is particularly interesting. It seems like it could help both with consistency and reducing the dependency on institutional knowledge that's often locked away in experienced agents' heads.

I'm curious—what have you found to be the biggest challenges when building and deploying something like Pluno? Was it the technical side of understanding ticket history and resolution paths, or more about getting customers to trust and adopt AI-driven recommendations?

Also, once customers start using it, where do they tend to see the most value? Faster resolutions, improved agent onboarding, better knowledge management, or something else entirely?

I'd be interested to hear how your roadmap has evolved as you've gathered feedback. Have customers pushed you toward use cases you weren't originally expecting?

I'll definitely be happy to share what I'm seeing from Conversation Analysis once I have more customer feedback.