Turning a 6,000-angel network into a self-learning investment engine.
A fast-growing angel investment platform supporting 6,000+ active angels and facilitating funding for nearly 300 startups, evolving into a multi-stakeholder ecosystem with LPs, investment banks, and VC firms.
What wasn't working.
A basic matching system couldn't keep up with deal-flow volume across thousands of angels, or the nuanced criteria that make a match worth an investor's time. Reporting was manual and took weeks to compile.
What we built.
Predictive matching engine
Machine learning models trained on investment history, risk appetite, and co-investment patterns to rank deals and predict investment likelihood.
Ecosystem intelligence
Predictive models assessing syndicate formation probability and funding readiness as new stakeholders join.
Automated reporting
Insight-rich reports with embedded predictive scoring, cutting reporting time from roughly two weeks to under two hours.
Built with.
What powered this build.
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