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Show HN: Algomo – Customer Support Powered by LLMs, Web and Neural Search (playground.algomo.com)
11 points by algomo 1109 days ago
Hello HN,

My team and I have developed a customer service platform, leveraging the capabilities of OpenAI’s GPT 3.5 and 4, Bing’s custom search, and internal document search functionalities.

Its key benefit is the ability to summarize and cite the top search results.

This AI system makes use of both public and internal search results to provide citations for specific facts. This not only brings more transparency to the generated answers, but also helps improve retrieval accuracy and the overall quality of the response.

We believe in the power of combining the intuitive and transparent UI of web search with the intelligence of large language models. The underlying search and indexing engine provides the necessary context while the interpretation is done by the large language models. To improve the quality of responses, we employ two LLM calls - one to generate multiple queries to retrieve diverse results, and another to generate the final response.

We’ve developed a playground where you can quickly test our system on any website - visit playground.algomo.com.

It’s like integrating perplexity.ai into your customer service. On our full platform at algomo.com, you can use multiple websites and even upload your own documentation from Notion.

We’re gradually developing new features for example allow users to view and edit the generated search queries, integrate other APIs, and AI cobrowsing.

Curious what the community thinks, and what features come to mind when you use this interface.

3 comments

Interesting! Can I use it in my eshop website and test it? I want to help my customers find what they want faster
yes of course - the playground is only a demo. Just signup at app.algomo.com
What plans are there to make transparency better for users?
For visitors - Show: A) full sources B) which part of each source was used to generate the final response C) which sources were omitted D) full rationale behind all decisions (eg how did the LLM come to answer) E) all queries used to generate the answer

For agents A) Enable saving of valid responses right from the conversations page B) Surface possible conflicting sources (eg pricing in the website being different to that of documentation)

What is the difference against competitors eg Intercom Fin?
The main difference is that we can combine information from multiple sources (online search, pdfs, notion pages etc).

As far as I know, Fin is only trained on help articles, which of course means that one has to have content already in there.

From what I’ve seen the quality of responses is much better on the algomo playground than via Fin.