We closed Ridgepoint Fund I in April of this year — $52 million, raised from a small group of institutional LPs who share our conviction that enterprise AI infrastructure is being built in the wrong order. This letter is for the founders who are building the right things at the wrong time, which is to say: the founders we want to back.
The thesis is simple, even if the work isn't. Enterprise teams are deploying AI faster than the infrastructure underneath it was designed to support. The model layer is moving. The orchestration layer, the observability layer, the governance layer, the data-plumbing layer — those are still being invented. The companies that own those primitives will be running underneath every enterprise AI application built in the next decade.
Why we believe this is the right moment
The enterprise software market has reset once in the last twenty years in a way that created a generation of platform-level companies. The shift to cloud — SaaS, managed databases, containerized deployment — opened a window in which new infrastructure categories were created from scratch. That window lasted roughly from 2008 to 2016, and the companies that built in it are now the plumbing of modern enterprise software.
We believe the AI adoption wave is creating a second window. The categories being created are different — orchestration for AI-in-the-loop workflows, observability for probabilistic systems, governance for LLM deployments, CDC infrastructure for AI-native data access — but the pattern is the same: a technology shift is outrunning the infrastructure layer, and the companies that solve the infrastructure layer early will compound that advantage for years.
Who we are looking for
Founders who have felt the problem personally. Not founders who researched a market and concluded it was large. Founders who spent a year trying to solve this problem inside a real enterprise environment, failed to solve it with existing tools, and left to build the right solution for everyone else. That distinction in founder motivation is not subtle — it shows up in the product decisions, the customer conversations, and the willingness to hold an unpopular architectural position when the market is pushing toward a shortcut.
We write checks at Seed and pre-Seed, and we lead. Our check size is sized to be meaningful to the company at that stage: small enough that the founders don't feel like they're giving up control to make the math work, large enough that we're a real partner with skin in the game. If that sounds like the conversation you're having, we'd like to hear from you.