Evolution Timeline: From Insight to Infrastructure

    • Invest in Series A–C startups with strong traction

    • Focus on GTM, revenue, and operational strength

    • Begin capturing data on founders, rounds, and performance

    • Vexter starts logging foundational data

    • Centralize and structure deal data

    • Enrich each opportunity with founder, traction, and sector signals

    • Build scoring systems to rank and qualify companies

    • Vexter uses this enriched data to train machine learning models that spot patterns in successful deals

    • Vexter powers sourcing and investment decisions

    • Surfaces high-potential startups using real-time behavioral and digital signals

    • Scores founders and teams based on historical success indicators

    • Enables fast, data-driven decisions across sourcing, check sizing, and follow-on

Founder DNA Model


We’re developing a model to identify the traits we believe drive founder success — such as follow-through, resilience under pressure, clear communication, and long-term vision.
Using AI, Vexter will extract these signals from sources like LinkedIn, X, Medium, and email behavior — then validate them over time against real outcomes like fundraising success, hiring quality, and go-to-market execution.

In Phase 3, V11 becomes a fully automated VC platform — using machine learning to understand what makes the best founders and teams scale.

We’re building the Founder & Team DNA Engine to go beyond pitch decks and analyze real behavioral signals.

Team Evolution Engine


Strong teams evolve. Vexter will track how teams develop — whether they’re becoming more structured, improving communication, or showing emotional stability.
As more data flows in, Vexter will learn to recognize not just promising teams — but fast learners that consistently outperform the market.

With Phase 3, V11 doesn’t just back strong founders. We use Vexter to help us spot the ones built to lead.

How We Map the AI Ecosystem

  • Broad, foundational models like GPT-3 and DALL·E-2 that handle diverse outputs such as text, images, and speech.

  • Models fine-tuned for particular tasks, offering enhanced performance in areas like ad copywriting or e-commerce image generation.

  • Highly specialized models trained on proprietary data to cater to niche requirements, such as scientific article writing in a specific journal's style.

  • Frameworks and tools that facilitate the integration and operation of AI models within various applications.

  • End-user applications that leverage AI models to deliver specific functionalities, such as AI-driven writing assistants or customer service bots.

Investment Strategy

“The value is at the application layer” - Pat Grady (Partner at Sequoia)

At V11, we invest in AI companies at the application layer—the critical interface where AI technologies directly engage with end-users. This is where AI transitions from theoretical potential to practical utility, embedding itself into daily workflows and delivering tangible value.

“Every monopoly is unique, but they usually share some combination of proprietary technology, network effects, economies of scale, and branding.” - Peter Thiel, Zero to One

The Application layer is pivotal for establishing enduring user relationships and creating defensible moats through unique user experiences and data feedback loops. We focus on companies that harness compounding data advantages—leveraging network effects, proprietary datasets, and self-reinforcing data loops—to enhance product performance and defensibility.

From Signal to Scale: Backing Defensible AI at the Application Layer

From Signal to Scale: Backing Defensible AI at the Application Layer

Where We Invest

  • AI-driven systems operating in the physical world, enabling real-world automation.

  • Sector-specific platforms solving deep domain problems.

  • AI-driven systems operating in the physical world, enabling real-world automation.

How We Invest

  • Series A-C

  • United States

  • $500K - $5M