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A search-driven predictive analytics platform

A search-driven predictive analytics platform

Our team consists of MIT/Stanford mathematicians, computer scientists, and engineers who have built a platform that allows a natural language speaker to interrogate raw data to generate insights and predictions, using voice or written word. For example, a user can type, “How many loans will default this year” and the system will produce a model on the fly to identify the loans that are likely to default. This product will quantify a company’s future success by aiding in the forecasting of their business on all levels, overcoming their competition. The technology is vertical-agnostic and domain-agnostic, but we believe a successful go-to-market will depend on identifying a vertical industry segment/domain into which we can sell and generate network effect.

To date, 5MM has been spent on product R&D. Clean cap table; self-funded. Our nearest competitor generates 1.5 billion dollars in gross income.

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Founder, Serial Metrics Harvard Business School; Zuli.io; Eventbrite; IBM