11–20 of 63 posts
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?
I thought startups wrapping prompts would require something a more complex than semantic analysis, which is literally what this is. And for 500 bucks. Wow. Props for being able to sell this. I don't get the appeal of the UI, why is it so complex/convoluted.
I thought startups wrapping prompts would require something a more complex than semantic analysis, which is literally what this is. And for 500 bucks. Wow. Props for being able to sell this. I don't get the appeal of the UI, why is it so complex/convoluted.
lol i wish it was just wrapping prompts but things got harder once our customers grew bigger, we had to build queues. we had to do context management for bigger conversations and bunch of metadata fields started coming in per customer.
Without using agnost, what are some basic SQL queries I can run on my data to find outliers I'd otherwise be missing? 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
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codex is great for like a one-time/overview analysis on a handful of transcripts. we usually serve to companies where the volume is >10k messages & continuous ingestions + with claude/codex it messed up this + metadata linking of the user like what plan are they on, when is it expiring, etc. although we had a few customers who come to us after running this for a while so at smaller volume it does work well.
i mean i would get codex to build everything you just described
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lol i wish it was just wrapping prompts but things got harder once our customers grew bigger, we had to build queues. we had to do context management for bigger conversations and bunch of metadata fields started coming in per customer.
It's still a prompt, it's just not a static one. Either way props for building a company from it.
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It's still a prompt, it's just not a static one. Either way props for building a company from it.
How is it just a prompt? Like hey, I hate AI companies with a passion but I think this is a lot more than just a prompt.
Earlier quoted context omitted.
lol i wish it was just wrapping prompts but things got harder once our customers grew bigger, we had to build queues. we had to do context management for bigger conversations and bunch of metadata fields started coming in per customer.
It's still a prompt, it's just not a static one. Either way props for building a company from it.
Earlier quoted context omitted.
How is it just a prompt? Like hey, I hate AI companies with a passion but I think this is a lot more than just a prompt.
I don't hate AI companies. The key value proposition is gather data > feed it to AI for semantic analysis (does the actual work, is a prompt) > display it in a UI
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.