Covector Syndicate

Capital and technical validation from first principles.

We are a DACH-based angel syndicate writing early-stage checks for founders tackling challenging deep tech problems at the frontier of what is computationally and physically possible.

Deep tech requires deep diligence. Because our background spans pure physics, production machine learning at FAANG, and biological AI foundation models at a leading TechBio scaleup, we evaluate ground truth. We provide institutional VCs with the technical signaling required to back complex frontier architectures.

Portfolio Value-Add

Ground Truth Over Pattern Matching

First-Principles Technical Sparring

Direct, 1:1 architectural reviews of your core loss functions, model constraints, physics inputs, or biological data pipelines—before you commit compute or lab resources.

Research-to-Production Navigation

A practical roadmap for translating early academic code into resilient, production-grade infrastructure, drawing on scaling practices from FAANG and high-growth TechBio environments.

Technical Validation & Signaling

A thorough technical de-risking process that gives institutional lead VCs and generalist funds the conviction to price and back your round.

Execution & Talent Intelligence

Off-the-record guidance on structuring high-performing engineering teams, navigating research-to-engineering dynamics, and evaluating critical early technical hires in the DACH ecosystem.

The Syndicate Leads

Thomas McColgan

Thomas McColgan
Engineering Manager at FAANG | Ex-TechBio ML Engineer | Physicist
Specializing in physics-informed ML, biological foundation models, and scaling high-performance engineering teams.

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Co-Invest or Pitch Us

We partner with institutional seed funds looking for technical validation, and founders tackling challenging deep tech problems. We respond to every technical submission within 48 hours.

Connect with Covector

Join the Syndicate

Capital is necessary but not sufficient. We are actively expanding the syndicate, and we prioritize members who bring genuine technical depth, operational scars, or domain-specific ground truth—not just check size.

If you have a strong technical, research, or operating background and can add real signal to founder diligence—in physics-informed ML, computational biology, robotics, or scaled ML infrastructure, or an adjacent frontier-tech domain—we want to talk. Members are expected to contribute value-add, not just capital.

Apply to Join