A private equity-backed education platform operating 100+ preschool sites had grown through M&A, leaving disparate data systems that could not communicate with one another.
Leadership had no real-time visibility into staffing levels across sites. Determining which sites were over-staffed required hours of manual data pulls and Excel analysis, preventing tight labor management in an industry where labor is the only significant variable cost.
SaxeCap integrated data from dozens of disparate systems, trained a machine learning model on historical attendance data to predict hourly student counts by age level, and built an operations research optimization model that recommended real-time staffing adjustments to site principals while maintaining state-mandated ratios.
Labor productivity at the preschool platform increased by over 50% following deployment of the AI-driven staffing optimization system.
EBITDA margins expanded by more than one-third (33%+), transformative for an industry where labor is effectively the only variable cost.
SaxeCap's standard approach delivers demonstrable EBITDA uplift within approximately two months of engagement start, including diligence, analysis, and first system deployment.
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