iSearchFund for AI-driven FinTech-powered Search Funds

July 21, 2024

by a searcher from Stanford University in San Mateo, CA, USA

Hi everyone, I'm a parallel tech entrepreneur and investor, so I adapted some of our recent work with a view to creating a fully integrated digital operating system for the search fund universe, ready to leverage the coming age of AI.

It's early, but I'd love some early collaborators — folks who have wanted a platform and a more structured protocol to transparently and parallelly scale the M&A business globally.

Early draft on intent: https://www.awake.ventures/p/isearchfund-revolutionizing-search-funds-for-the-age-of-the-ai-powered-internet-6wjp4av54v7g9gmt?refBy=Mzg4YmE4ZWViMw

Sign up here: https://iSearchFund.com

I am looking forward to hearing from you all!

Regards,
Amit

Reply by a professional
from University of Wisconsin in Madison, WI, USA

The concept is interesting but how are you dealing with the data challenge? 1. The "rollup" strategy of identification will require a elaborate registry of a host of businesses and their connected ancillaries. But how do you actually collect and label these? 2. Even after collecting and labeling -- the harder part of the process is the actual depth inside the businesses books which are often not well kept. If you're going with a rollup strategy this means that you're actually dealing with a lot of obfuscated/fragmented data. I'm strongly in alignment with the network goal -- I just think that you need a system to integrate businesses who want to sells data in cleanly as a part of that network. Perhaps you've solved this already (if you have, awesome!) But it has been something on my mind for a while. My guess is the best path is to be a distinct vertical and capture sellers on it.

Reply by a professional
in Ormond Beach, FL, USA

Would love to chat / learn more - quick question - would there be any benefit to leveraging private data within a “clean room / llm env”. For example a way to allow end users prompt and interact with proprietary data / apis, etc privately- without risk of exposing confidential information to the host of the llm / experience?

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