Cocoon Capital, an early-stage VC firm investing in enterprise and deep tech startups across Southeast Asia, wanted a smarter, more predictive way to identify high-potential founders and reduce investment risk. With only six new deals per year, every choice matters. Marlee’s insights gave the Cocoon Capital team a data-driven lens to assess founder blind spots, support portfolio teams, and build success models that drive better outcomes across their investment lifecycle.
Cocoon Capital is an active, early-stage venture capital firm investing in enterprise and deep tech startups across Southeast Asia. With a high-conviction approach, they work closely with founders post-investment, only backing up to six new startups per year to ensure hands-on support and partnership.
As an early-stage venture capital investment fund, Cocoon Capital is no stranger to high risk. Globally, 95% of startups fail (Crunchbase Insights), and with little data to assess the level of risk for the investment, it is challenging to predict which startups will succeed.
Cocoon Capital invests in only six companies per year, meaning each pick must be spot-on. The team sought a data-driven way to minimize risk and maximize return on investment.
Using Marlee’s Motivational Analysis and entrepreneurship research, Cocoon Capital gained powerful insights into founder blind spots and team dynamics. Marlee compares each profile to its proven success models, offering clarity on potential pitfalls and strengths. These insights are used in due diligence, founder coaching, and to guide investment decision-making.
Marlee gives Cocoon Capital a consistent methodology to evaluate and support founders. The tool has helped build stronger founder awareness, inform team formation, and coach founders through blind spots. Several portfolio companies have also adopted Marlee to shape culture and performance from within, further extending its impact.
Cocoon Capital continues to integrate Marlee into its due diligence and post-investment support. By combining founder data across years, the team is now building its own pattern recognition of what success looks like, creating deeper value for both investors and the startups they back.