[laalshaitaan]

yea, at our volume which we still consider small as we've been able to figure out a way with llms & embeddings, its still fine. + we onboarded a voice ai company with more than 2 hour calls and thats when it was super hard to solve since there were so many elements to consider.

model drifting is something a lot of folks do face after 5th/6th turn as per my understanding and it usually the median, how did you tackle it if you have yet?

also yea, thats why we went for a per customer taxonomy than a general one, yeilded better results + easier to improve upon.

[kianN]

To clarify, I wasn't criticizing your approach or product, more responding to the people dismissing the problem you are solving.

Regarding my experience, I have done a fair amount of work in the contact center space with long calls. I used statistical Bayesian approaches which I found to be much more resilient especially on long documents than embeddings/transformers. It also provided a joint modeling foundation for classification with much lower label requirements than BERT or traditional ML.

[laalshaitaan]

im hearing this for the first time and damn! i just told this to my cofounder/cto and he said hes gonna give this a shot in the coming days.

damn, i read bayesian in statistics like years ago, never thought itll come back this way

[kianN]

Happy to chat more in depth if more details would be helpful. I think my contact info is accessible from my HN profile.