Key Takeaways

  • Venture investors typically discover enterprise software adoption months after enterprise product leaders start deploying it internally.
  • Mighty Capital tracks a global community of 600,000 product professionals to identify enterprise purchase intent before revenue shows up in standard deal databases.
  • The firm runs PSIS, an internal software stack that applies machine learning, natural language processing, and large language models to convert community discussions into structured investment signals.
  • Moatti filters specifically for product buyers rather than tool builders, isolating true enterprise budget commitments from developer hype.

Enterprise Buyers Spot Software Demand Before Investors

Venture sourcing models often rely on lagging indicators: founder pitch decks, demo day applications, and cap table announcements. By the time a software startup shows up on traditional investor screens, corporate engineering and product departments have already spent months testing, buying, or rejecting the tool.

SC Moatti, Managing Partner at Mighty Capital, founded the firm on the observation that corporate operators sit directly at the point of adoption. As Moatti points out: “Product people when you think about it really simply, they're always the first ones to know about innovation because they have to bring it to their corporation to scale. That's their job, right? So they're always the first ones to see that.”

By contrast, early-stage capital allocators face an information delay. “The challenge that investors have which I found out when I started investing is investors are actually the last ones to know about that innovation,” says Moatti. “And we said at Mighty Capital, we said we are going to change that.” Sourcing directly from the buyer side flips this delay into an information advantage.

Turning 600,000 Product Conversations into Commercial Data

Tracking enterprise software adoption requires separating genuine enterprise budget allocation from developer enthusiasm. Many open-source and developer tools generate high social chatter without ever securing corporate purchase orders.

Mighty Capital monitors a community of 600,000 product professionals across industries and regions. To process this volume, the firm developed an internal intelligence platform called PSIS. Moatti explains their data processing setup: “We capture all these conversations across industry, across geography. We have our own homegrown AI infrastructure that we build. Use AI, a combination of machine learning, NLP, and LLM technology to turn that into signals that are literally readable by humans.”

The critical distinction in Mighty Capital's model lies in who they track. The firm ignores developer noise and isolates enterprise decision-makers who control software budgets. “And so we're not looking at the product builder who's actually building the product,” Moatti explains. “We're looking at the product builder who's buying the product in order to implement it in their own organization. So those are commercial signals that are driven from the edge of innovation.”

When a pattern of enterprise product leaders starts discussing specific integration hurdles, vendor evaluations, or rollout timelines, the software flags an emerging commercial winner. This approach, which Moatti calls the Product Alpha Effect, lets the firm evaluate startups based on real corporate procurement pipelines rather than polished pitch decks.

Why It Matters

Traditional venture capital sourcing relies heavily on warm introductions and trailing financial metrics, which inflates entry valuations in competitive rounds. Tracking real-time buyer behavior across hundreds of thousands of enterprise operators changes the underwriting equation. Investors who identify procurement patterns before revenue registers on accounting statements can secure early allocations at disciplined entry prices. As software distribution channels crowd, proprietary demand-side data will separate alpha generators from momentum chasers.