Taking a deep dive into machine learning, semantic search and how enterprised can bring AI in-house.

The more you dive into AI the more your brain simply explodes. You can’t possibly absorb it all. One thing we have learned though, is that we have this public AI, like ChatGPT, Gemini etc, and then this concept of a closed or private AI. A private AI uses the magic of a public LLM, but keeps the data local, on your own infrastructure. This means that confidential and proprietary information stays behind your firewall, and the LLM is never trained on your stuff. This is done by a super cool DB architecture call RAG.

RAG stands for "Retrieval-Augmented Generation," and it basically AI enabled’ s your own data. In short, because it’s universe is only the data you provide, it doesn’t make stuff (hallucinates) - which is a common complaint with public LLMs. It’s kinda like looking up information on the web (which is everything), versus looking up information in a library (which is only what you see).

Here’s the pitch… for enterprise use, RAG is a big deal. Most companies have tons of documents, data, and files, and it’s hard for anyone to find the exact info they need quickly. A RAG system can search through all that information, pull out what’s most relevant, and then use AI to explain or summarize it in a way that’s easy to understand. That means employees waste less time digging through files, customers get better answers faster, and the business runs more smoothly.

We now have our vision and north star. XFinLabs is going to bring AI to the enterprise. Cheers.