TROVE Predictive Data Science

TROVE Predictive Data Science emerged as a spin-off from CUBRC, a Buffalo, New York-based non-profit research and development company. The firm was established to commercialize a sophisticated data fusion technology developed at CUBRC. One of the early founders was Isaias Sudit, who, along with colleagues like CTO Adam Stotz, saw an opportunity to apply their expertise in data analytics, originally honed in defense and intelligence contracting, to the commercial sector. Recognizing they lacked direct experience in the utility industry, the founding team established an advisory panel of utility executives to guide the development of their product portfolio. The company, formerly known as GridGlo, secured seed funding around 2014, which facilitated key hires like Adam Stotz moving to a full-time position. A significant milestone was its acquisition by E Source, a research and advisory firm for the utility sector, in February 2020. This acquisition integrated TROVE's artificial intelligence capabilities with E Source's extensive industry knowledge and client base.

TROVE operates in the predictive analytics market, delivering artificial intelligence and data science solutions primarily for the utility industry. Its business model revolves around a Software-as-a-Service (SaaS) platform, supplemented by a consulting arm known as the "Science Squad"®, which helps clients deploy and leverage the technology effectively. Revenue is generated by providing these software and service solutions to data-intensive clients like public utilities and telecommunications companies. The company's platform addresses complex, high-value challenges by fusing a client's internal data with thousands of attributes from external third-party data sources. This enables TROVE to offer predictive insights for use cases such as demand forecasting, fraud identification, customer segmentation, and optimizing vegetation management to reduce wildfire risk. For instance, the platform can create predictive models for lightning strikes or avian patterns to optimize the placement of protective equipment on the distribution grid. Actionable insights are delivered to end-users through straightforward web interface tools, designed for operational use.

Keywords: predictive analytics, data science, utility industry, AI solutions, machine learning, data fusion, CUBRC spin-off, E Source acquisition, GridGlo, customer segmentation, demand forecasting, vegetation management, operational risk, fraud identification, big data analytics, utility data, SaaS platform, energy sector, data modeling, business intelligence