Eniac Ventures

My team built a queryable events database (QEB) for VCs, using AI. By the end of this post, you’ll be able to build one yourself to answer prompts like: “Show me all the AI related events happening in SF this week.” Or: “What are the top VC networking events in New York next month?” 𝟭. 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆𝗶𝗻𝗴 𝗘𝘃𝗲𝗻𝘁 𝗕𝗼𝗮𝗿𝗱𝘀 𝗜𝗻 𝗢𝘂𝗿 𝗖𝗶𝘁𝘆 🔎 Sorry SF, going with NYC for this example. We’re going to use Luma, Gary’s Guide, and Eventbrite. It doesn’t matter where you live, though. You’ll be able to use this guide to build your own version! 𝟮. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗥𝗼𝗯𝗼𝘁 𝗦𝗰𝗿𝗮𝗽𝗲𝗿𝘀 𝗪𝗶𝘁𝗵 𝗕𝗿𝗼𝘄𝘀𝗲 🤖 Browse AI (https://www.browse.ai/) lets us turn any website into an API. Even if they don’t have one. Without writing a single line of code. For now, making a free account is good enough. 𝘉𝘶𝘪𝘭𝘥 𝘠𝘰𝘶𝘳 𝘙𝘰𝘣𝘰𝘵 Check out this brief Loom we made showing how easy it is to scrape data with Browse: https://lnkd.in/eVZyf8GR. We created our first AI data scraping robot in minutes. 𝘐𝘯𝘵𝘦𝘨𝘳𝘢𝘵𝘦 𝘞𝘪𝘵𝘩 𝘎𝘰𝘰𝘨𝘭𝘦 𝘚𝘩𝘦𝘦𝘵𝘴 Now we want to throw that data into a spreadsheet. We clicked on Google Sheets, chose our Google Account to sync up with, selected the spreadsheet, and mapped the data to the right fields (see image 1) Now that we’re all finished in Browse, this is the final product (see image 2) 𝟯. 𝗕𝗮𝗰𝗸 𝗧𝗼 𝗧𝗵𝗲 𝗟𝗮𝗯 (𝗖𝗵𝗮𝘁𝗚𝗣𝗧) 🧪 Now we need to build an app that will call the OpenAI API in order to query our mini database for the events the user is interested in attending. If you’re not familiar with our process, check out this recent edition of AI for VC on building an AI for VC app: https://lnkd.in/emjXM5KF Here’s the prompt we started with (see image 3) Make sure you provide the name of the CSV file for ChatGPT AND upload that CSV file to the Pycharm program so it’s readable. The output from the code interpreter worked in just a few tries, and we were ready for a demo. Here’s the key: Test the code out and report back screenshots of any errors to ChatGPT so it can auto correct its mistakes. Reach out to us if you’d like to see the final code! It’s only 85 lines. 𝟰. 𝗣𝘂𝘁𝘁𝗶𝗻𝗴 𝗜𝘁 𝗔𝗹𝗹 𝗧𝗼𝗴𝗲𝘁𝗵𝗲𝗿! 🥪 We ran the script in Pycharm with the terminal command “streamlit run events.py”. This is what it looks like in the browser (see image 4) So, we typed in: “Find all events related to financial technology (FinTech) happening this week in New York City.” Here are some of the results (see image 5)

🚨 Alert, someone in VC is actually hiring! 🚨 I'm looking for a chief of staff to be my right-hand human. It's a great opportunity to break into venture and get broad exposure to everything I do. See below to learn more and apply https://lnkd.in/eqD2k4yc

Congrats again to the team at Tradespace for raising a seed! You can read more from about why we invested here: https://lnkd.in/e7JDxebR

The Faux First Mover Advantage. Check out this guest post on how being first isn’t as good as people thinking you were first By Nir Zicherman who cofounded and sold Anchor to Spotify https://lnkd.in/e4M53zns

Eniac is excited to announce participation in Tradespace’s $4.2M seed funding round as the company looks to unlock $1 Trillion In #IP value with its #AI-powered Commercialization Platform. Tradespace’s platform is trained on the largest set of IP and innovation data to help IP owners evaluate new inventions and generate higher-quality IP - and has already helped organizations unlock more than $100M in IP value. Read more below. Tradespace, Scrum Ventures, Amplo, Hike Ventures, 500 Global

We’re thrilled to share the news that Fabi.ai has officially launched, and that it’s raised $3 million in seed funding led by Eniac Ventures. Fabi.ai is tackling data democratization, allowing business users to understand their data without using technical resources. It connects to existing data systems and uses AI to automate the process of answering queries from product management, marketing, customer success, or elsewhere in the company — streamlining that process while still giving data teams control over the ultimate output. This is a problem Fabi.ai’s founders Marc Dupuis and Lei Tang have seen at a number of companies, including when they worked together at Clari, where Marc was director of product management and Lei was chief data scientist. Marc found himself constantly sending data questions to Lei, who would have to write SQL queries to find the answers. Eventually, the two of them decided to find a more efficient way to help all teams get the information they need — freeing data scientists from monotonous and repetitive tasks so they can focus on more strategic work. We’re thrilled to be partnering with our friends at Outlander VC on this investment, and even more thrilled to be backing Marc and Lei. Although Eniac and Fabi.ai are located on opposite coasts, we were able to close the deal over a three-hour lunch meeting in Vancouver, where we solidified our conviction that they were the perfect founders to tackle this issue. Beyond seeing the problem, Marc and Lei understand the technology needed to solve it. While the latest generation of large language models can translate natural language into SQL code, company data is often messy, requiring you to build deeper connections to the data stack if you want to access it in an automated way. We see Fabi.ai as part of a larger shift towards the democratization of data for nontechnical users. We’ve backed a number of companies working to improve the data stack, from infrastructure companies like Model-Prime to companies that empower data scientists like Pienso and even those that use data to transform healthcare like 1upHealth. By simplifying the interface needed to access this data, Fabi.ai isn’t just making teams more efficient — it’s making data accessible to virtually anyone in an organization. Read more about it from the Fabi team: https://lnkd.in/eYAJFjXC