How investors could discover your startup faster and more effectively? 🤔
Mohamed Lotfi Sassi
7 replies
Fundraising is often time-consuming and full of uncertainty. But what if there was a way for investors to discover startups more efficiently, based on real-time pitches and tailored matches?
At OLINDIAS, we’ve been exploring the concept of an investor’s startup discovery hub—a platform where startups can upload product demos and elevator pitches, and investors can browse through and find matches that align with their interests. We’re calling this platform Recolyse.
Here are a few questions we’d love your input on:
• 💡 How do you feel about the idea of investors browsing video pitches instead of relying solely on pitch decks or meetings?
• 🤖 Could AI-driven matchmaking between startups and investors streamline the discovery process?
• 🔄 What would make a platform like this more effective for both investors and startups?
We’re curious to hear your thoughts about whether this approach could make fundraising more efficient and help startups gain exposure to the right investors more quickly.
Let’s start a discussion! We’re live on Product Hunt, and we’d love for you to check out the idea and share your feedback:
🔗 https://www.producthunt.com/posts/recolyse
Replies
Menelaos Kotsollaris@mkotsollaris
Launching soon!
Very neat idea. I am wondering, what's your data provider to feed LLMs the data they need?
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I believe this idea is a game-changer, saving both time and money for investors and startups. Investors can quickly discover the best deals through AI, while startup owners will receive top offers with minimal effort, avoiding the need for extensive pitching and event attendance.
I think an effective way for investors to discover startups faster is through AI-powered matchmaking platforms that analyze both the investor's interests/criteria and the startup's business data to suggest high-potential matches. The key is having robust data on both sides to train the AI models. Interested to hear others' thoughts!
Scraping and processing publicly available data yourself can be a good way to feed LLMs. You can set up web scrapers to collect relevant info from company websites, news sites, social media, etc. Then clean and structure the data before feeding it to your models. Takes some manual work but gives you full control over your datasets. Outsourcing to data providers is another option if you don't want to do it in-house.
A good data source could be web scraping relevant startup and investor websites to gather current info on active startups and investment focus areas. There are some affordable web scraping tools and services out there to automate extracting the data needed to keep LLMs up-to-date. Just an idea!
Sound interesting to sign up !
maybe not faster but we can make effective and comfort for us