Catapult

catapult.vc

VCMatch tracks Catapult as an investor focused on AI/ML, Robotics, and Hardware companies and Pre-Seed and Seed rounds. Upload your deck to compare your startup against the full private matching profile.

Check Size

Not specified

Stages

Pre-Seed, Seed

Focus Areas

AI/ML, Robotics, Hardware

Geography

Not specified

Investment Thesis

Catapult Ventures is an operator-led, seed-stage deep-tech fund that invests where hardware, embedded systems and software converge to build real-world autonomous and automation systems. The firm emphasizes practical, revenue-focused engineering and interdisciplinary technical advantage — backing founders who can translate advances in AI, sensors, actuators and compute into robust products for large industrial and commercial markets (examples in the portfolio include efficient AI at the edge, robotics, VTOL aircraft, embedded AI processors and automation software).

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Investment Focus

Who should take a closer look

Catapult is most relevant for founders raising Pre-Seed and Seed rounds in AI/ML, Robotics, Hardware, and Semiconductors.

Focus statement

“We invest in pre-seed and seed-stage startups at the intersection of hardware, embedded systems, and software.”

Value Add

Operator-founded / domain experience: founders (Darren Liccardo and Rouz Jazayeri) have deep, hands-on backgrounds in autonomous systems, aerospace, automotive and product engineering — the site says they “have over three decades of operational experience” and “approach investing from the perspective of having been in the trenches.”
Hardware + embedded + software thesis: explicit emphasis on cross-domain synergies — “leveraging the synergies between these domains can enable innovations that otherwise would not be possible.”
Stage and capital strategy: focus on pre-seed and seed, frequently lead rounds but also syndicate; they “reserve significant capital for subsequent funding rounds” and tout “strong relationships with later-stage investors” to help catalyze growth.
Practical, product-centered selection: preference for founders building revenue-generating, real-world systems and who demonstrate grit and scrappiness (Rouz calls out “grit, scrapiness, and willingness to get their hands dirty”).
Technical perspective on safe, robust autonomy: they emphasize engineering trade-offs beyond pure end-to-end deep learning (their 'BEYOND DEEP LEARNING' section advocates mixing traditional and new algorithmic approaches and human-in-the-loop where appropriate).