Let's be honest: most "AI" chatbots are just glorified search bars with a chat UI. They match keywords, pull from a FAQ, and fail the moment a user asks something slightly complex.
I learned this the hard way. A few months ago, I was a full-stack dev at an e-commerce company, and we were drowning in support tickets. We were spending 6+ hours every day answering the same repetitive questions about pricing, features, and "how-to"s. It was killing our productivity.
We looked at existing solutions, but they were either the keyword-finders that frustrated users or complex enterprise tools that cost a fortune. So, we decided to build our own.
But we quickly found that even standard RAG solutions struggle with complex questions that require understanding the customer's context. We tried hundreds of techniques, but most were only good for simple Q&A.
Our key breakthrough was developing a custom implementation on top of RAG that's designed to understand user intent and context. The goal isn't to deflect users by linking to an article, but to provide a direct, accurate answer that genuinely solves their problem. For that project, it's now accurately handling ~80% of inbound queries.
We've decided to turn this into a product and just opened the waitlist. We'd love to hear what this community thinks. Is this "glorified search bar" problem something you've experienced too?
If so, is there anything preventing them from enshitification like has happened to search engines? Or five years from now will we be swimming through dregs again as spammers, scammers, and advertisers turn the results to sludge?
Hey HN, SR, co-founder of Kasp here.
Let's be honest: most "AI" chatbots are just glorified search bars with a chat UI. They match keywords, pull from a FAQ, and fail the moment a user asks something slightly complex.
I learned this the hard way. A few months ago, I was a full-stack dev at an e-commerce company, and we were drowning in support tickets. We were spending 6+ hours every day answering the same repetitive questions about pricing, features, and "how-to"s. It was killing our productivity.
We looked at existing solutions, but they were either the keyword-finders that frustrated users or complex enterprise tools that cost a fortune. So, we decided to build our own.
But we quickly found that even standard RAG solutions struggle with complex questions that require understanding the customer's context. We tried hundreds of techniques, but most were only good for simple Q&A.
Our key breakthrough was developing a custom implementation on top of RAG that's designed to understand user intent and context. The goal isn't to deflect users by linking to an article, but to provide a direct, accurate answer that genuinely solves their problem. For that project, it's now accurately handling ~80% of inbound queries.
We've decided to turn this into a product and just opened the waitlist. We'd love to hear what this community thinks. Is this "glorified search bar" problem something you've experienced too?
Link: https://www.kasp.chat/
What about vector databases?
Cosine similarity search
UI looks cool. The white text on the clouds is almost impossible to read. (
If so, is there anything preventing them from enshitification like has happened to search engines? Or five years from now will we be swimming through dregs again as spammers, scammers, and advertisers turn the results to sludge?