How far can I get with just keywords, common phrases, boring traditional analysis?
Depending on what I measure there, when is the right time for me to consider upgrading to something like Agnost/what is a specific example of what it will find that traditional/rigid analytics approaches will miss?
interestingly, even embeddings seem to bucket "no" and "nooo!" somewhat similar, but are pretty different when viewed from a user satisfaction perspective.
A sweet spot on moving to Agnost is the time when you get higher inflow of conversations you can't manually read or listen, and want to clusterize them into things which matter, with the outliers highlighted
However privacy is central in a service like this and I think you should probably beef up your representation of how you deal with that.
eg. "We use each customer’s data only for that customer" - well that customer may have hundreds of staff; how are they being consulted and onboarded wrt their own voices (or is that transcripts?) and messages being used in this way?
ofc you might argue that nothing in work is private but I do think you have some margin for improving the detail here.
Lovely name! I implemented profanity monitoring in my Hermes setup to identify "learning opportunities" for my agents. It is quite useful. If you are budget-conscious, one challenge is determining what is the smallest number of previous rounds that Hermes needs to correctly infer what it did wrong. Curiously, Claude Code is horrible at figuring out what it did wrong. I often read its memories, and they are rarely useful.
for profanity, did you define keywords or just let the agent figure out rage stuff?
how many rounds did you set for the hermes? claude doesnt work yea on its own, one of my friends set us up for their claude lol
although we had a few customers who come to us after running this for a while so at smaller volume it does work well.
Looking forward to your "show HN" post.
I don't get the appeal of the UI, why is it so complex/convoluted.