Rocketship.vc

rocketship.vc

VCMatch tracks Rocketship.vc as an investor focused on AI/ML, Agtech, and Marketplace companies, Seed and Series A rounds, Global, United States, and India markets, and $100k to $40m check sizes. Upload your deck to compare your startup against the full private matching profile.

Check Size

$100k to $40m

Stages

Seed, Series A, Series B

Focus Areas

AI/ML, Agtech, Marketplace

Geography

global, United States, and India

Investment Thesis

Rocketship.vc runs a data-first, engineering-driven investment process: they collect and analyze large-scale startup data to identify early-stage, high-potential companies worldwide and then continue to support those companies with ongoing data tracking and insights. The firm positions itself as a global, remote-first early-stage investor that uses “smart algorithms to identify and invest in global winners,” couples operator experience (Amazon, Walmart, NASA) with world-class data science, and emphasizes a tight feedback loop between analytics and investment decisions.

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Investment Focus

Who should take a closer look

Rocketship.vc is most relevant for founders raising Seed, Series A, and Series B rounds in AI/ML, Agtech, Marketplace, and Cloud Infrastructure, especially across global, United States, and India, with public check-size signals around $100k to $40m.

Focus statement

Data-driven, early-stage (seed to Series A) investing across global technology sectors — with notable activity in AI/data infrastructure, fintech, B2B marketplaces, deep tech (space, climate, semiconductors) and SaaS — and a remote-first approach to sourcing and supporting companies in 14+ countries.

Value Add

Proprietary scale of data: "We have data on over ten million companies all over the world."
Algorithmic sourcing: "We use smart algorithms to identify and invest in global winners."
Post-investment analytics: "After investing, we continue to track your data, compare it with your competition and the market and feed our insights back to you."
Operator + data science team: partners with deep operating exits and tech experience (helped create Amazon Marketplace, Mechanical Turk, @WalmartLabs; worked on NASA’s Mars Rover) alongside authors of a leading data mining textbook.
Authoritative domain expertise: "We wrote the most popular textbook on data mining. It has over one million downloads."
Engineer-investor model: engineers who build the algorithms also participate in investments to keep the feedback loop tight.
Global, remote-first footprint: investments across 14 countries and active role as a "bridge to the valley" for non‑Silicon Valley founders.