Lunar Ventures

Lunar Ventures is a quant-flavored deep tech fund operating out of Berlin with a focused mandate: the algorithmic and infrastructure layers underneath modern AI, including in healthcare. The firm explicitly positions itself for technical founders building foundational rather than application-layer companies, and its conversations tend to start with architecture and end with markets rather than the other way around.

The healthcare thesis covers AI/ML in healthcare and health IT software, with a bias toward companies building infrastructure, models, and tooling that sit below the user-facing application. That is a narrow but underserved slice of European health AI: most generalist funds want a clinical workflow story, while Lunar is comfortable with teams whose primary product is a model, a data pipeline, or a developer tool that other healthcare companies will eventually build on. Specific portfolio companies are not enumerated in the source material, so founders should request technical references in diligence.

The firm invests at Pre-Seed, Seed, and Series A with checks of two hundred fifty thousand to one million dollars. That size positions Lunar as an early lead or significant co-investor at Pre-Seed, rather than a Series A anchor. The geographic focus is Europe, with selective activity beyond. For technical health AI founders, the relevant question is whether the firm’s check fits the round and whether the partner-level technical engagement justifies giving Lunar meaningful ownership at the earliest stage.

The team is built around partners with backgrounds in machine learning, engineering, and quantitative investing. Specific partner names are not provided in the source material, and founders should clarify partner ownership of the healthcare thesis directly. The firm is small enough that partner attention is generally not a question.

The right founders for Lunar are deeply technical teams building infrastructure, model, or tooling layers for health AI, where conversations with technically literate investors materially improve the company. The wrong founders are application-led or commercially driven teams, where the firm’s lens may feel orthogonal to the actual operating problem. Founders should expect diligence on model architecture, training data strategy, evaluation methodology, and engineering team composition. Lunar tends to reward specificity and engineering humility, and tends to disengage from teams whose technical story does not survive a careful read by people who do this for a living.