Eniac Ventures

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

We’re kicking off a series of founder interviews focused on product-market fit — what those founders did right, what they did wrong, and what they’ve learned along the way. We’re starting the series with Ashish Nagar, founder and CEO of Level AI, a company that provides AI-powered tools to customer experience teams. We invested in LevelAI years before the current AI hype, and Ashish’s history in the industry goes back even further — he previously served as a product manager on the conversational AI team at Alexa. Here are a few of the main takeaways from our conversation: Focus on problems that customers will pay to solve. “Our general framework was, ‘Is this a big enough problem? Is this a top three problem for this person? Number one?’ Because a lot of times people say, ‘Yeah, this is a problem.’ But is it something big enough that you will write a check right now, if you had a magic solution? That’s very different.” Finding PMF is an ongoing process. “I think PMF is a little bit of a continuum in enterprise, because you can get tighter and tighter and tighter and tighter product-market fit or product-market alignment. But you need to have a kernel, some small product that works for that [ideal customer profile].” Be clear about what kind of customer you’re pursuing. “We do AI for call analysis, but a lot of people tell us, ‘Why wouldn’t you do it for Gong and Chorus for sales teams?’ The simple answer is, that’s a whole different buyer, that’s a whole different market, we just don’t sell to the salesperson. When you’re building a sales product, the go-to-market journey is so different than it is for a VP of a contact center or the director of a contact center. Their value proposition is different, their budgeting is different, their problems are different, the other technologies they integrate are different.” Don’t forget the meat and potatoes. “We ask ourselves these three things: Where is my core differentiator where nobody else is playing? The second is, where do I need to catch up with other products that people like? And the third one is, what are the table stakes, meat and potatoes features you need to play in the space? For an enterprise SaaS company, you need all three. If you don’t have the meat and potatoes, even if it’s a boring form workflow or something, nobody takes you seriously.” Read the full interview here: https://lnkd.in/eYbxNBPQ

If there’s one thing we’re focused on at Eniac Ventures, it’s helping our founders find product-market fit. The term gets thrown around so often that it’s easy to assume every founder knows exactly what it means. But like many simple, powerful ideas, PMF can take on different meanings depending on who’s using it. The most popular definition comes from a 2007 blog post by Marc Andreessen, who wrote that PMF “means being in a good market with a product that can satisfy that market.” Here at Eniac, we believe PMF is closely tied to the idea of market pull. As we wrote last year: "The true test is whether you’re getting 'pull' from the market, or if you still have to 'push' your solution onto the world. When we meet with founders who’ve found true PMF, they’re usually incredibly stressed — not because they’re hustling for the next sale, but because there’s so much positive word-of-mouth that they’re struggling to on-board all the new customers, and to scale the infrastructure needed to support them." Other signs that you have PMF: Customers are loyal, happy, and demanding new features at a rapid clip; willingness to pay isn’t an issue; and if it’s a B2B product, you see organic expansion within a customer organization. It’s also worth noting that finding PMF is one thing, while maintaining it is another: Just because you have it initially doesn’t mean you’ll always keep it. You have to continue iterating to meet growing demand and a diversity of customers, while also keeping the core product intact. Indeed, it’s important to remember that PMF isn’t purely binary. It exists in different degrees — it’s not just a question of whether you have it, but whether it’s stronger or weaker (one of our founders has talked about trying to make his product-market fit “tighter and tighter tighter”). Of course, where PMF becomes meaningful is not in a general description, but rather a specific, warts-and-all narrative: How a company does or doesn’t find PMF, with all the inspiration and missteps along the way. That’s why we’re excited to kick off Eniac’s PMF Q&A: An in-depth interview series with our portfolio founders, where they talk about their search for PMF and what they think they’ve learned in the process. These founders come from companies at different stages, in fields ranging from mobile analytics to robotics, connected only by the fact that we think they’ve found PMF. They don’t always agree with each other — or with us! — about what PMF looks like, but in our view, that’s part of the fun. We’re learning a lot from these interviews, and we can’t wait to share them with you. Come back next week for the first, with one of our smartest founders in AI.