Early-Stage Investing in AI Infrastructure.

AI Infrastructure isn't AI applications. It's the foundation: model training platforms, data infrastructure, developer tools, inference optimization, and the systems enterprises need to deploy AI reliably at scale. These companies solve problems at the layer where all AI happens, and they need investors who understand both infrastructure economics and enterprise adoption.

We've backed teams from earliest stage through IPO across compute optimization, data platforms, observability, model serving, and the tooling enabling developers to build and deploy AI. You get venture capital plus operational partnership: enterprise relationships that drive adoption, go-to-market expertise for platform companies, and technical credibility with CXOs and engineering leaders making infrastructure decisions.

Areas of Interest

Model Training & Optimization. Infrastructure for efficient model training, fine-tuning, or distributed learning at scale.

Data Platforms & Pipelines. Data infrastructure, data quality, or data systems enabling enterprises to prepare and manage data for AI.

Model Serving & Inference. Tools for deploying, optimizing, and managing models in production at enterprise scale.

Developer Tools & Frameworks. Developer-focused tools, SDKs, or platforms that simplify building and deploying AI applications.

Open Source Infrastructure. Open-source projects and companies building critical infrastructure that powers the AI ecosystem.

Observability & AI Ops. Monitoring, debugging, testing, or operational tools for AI/ML systems in production.

Enterprise AI Platforms. Integrated platforms or infrastructure enabling enterprises to build, deploy, and manage AI at scale.