The purpose of analytics and data science is to improve your work by deepening your understanding. The payoff for careful modeling and analytics is often a remarkably simple insight. The distillation of complexity into clarity is the art and science of how we work with data.

Fundamentally, we are focused on helping you think through the tradeoffs facing your organization. With our current funding and policy landscape, organizations are navigating greater uncertainty and perilous funding cuts. Better analytics helps answer how the social sectors can survive and thrive in this moment.

Allegheny County Department of Human Services

In partnership with a community of practitioners and researchers, our data science team used state of the art ML to understand and anticipate the impacts of involuntary mental health hospitalization on young people. Through this project we used predictive modeling to guide institutional partners away from coercive interventions and towards evidence-based practices that reduce harm.

Government SaaS Vendor in the Environmental Stewardship Space

Often, building analytics infrastructure is two parts engineering, one part relationship management. In almost every analytics engagement we've found organizations with disparate analytics teams. In many cases the teams are siloed; in the worst cases the teams are the result of rifts in the organization itself. In a recent engagement with a government SaaS vendor, we were able to deliver analytics infrastructure using AWS serverless best practices and, ultimately, repair the connective tissue within the organization while doing it.

Western Reserve Land Conservancy

In partnership with Western Reserve Land Conservancy, we developed a five-year business plan to sustainably scale Cleveland's Reforest Our City program, one of the longest-running urban forestry initiatives in the region. The engagement required translating a decade of operational experience across five program areas into explicit unit-level assumptions, grounded in market research and stakeholder interviews. We built a dynamic financial model that aggregates those assumptions into target financials, giving the organization a living planning tool — not just a static report — to test scenarios, clarify tradeoffs, and chart a path toward Cleveland's goal of 30% citywide tree canopy by 2040.