In data science, context is everything. Our process draws on both classic and emerging methods and is fundamentally responsive – assessing what an organization or community has, what it cares about, and what it needs.

We design systems and algorithms that are built to last. Whether developing social service machine learning infrastructure or using data to communicate the perspectives of marginalized employees, we create tools that make new things possible.

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.

Achievement First

In partnership with one of the largest charter school networks in the country, we used AI to detect inconsistencies in hiring and retention practices. We were able to quantify the extent to which the network's talent management practices were aligning with its mission and goals. The work involved developing customized data infrastructure to support predictive models, and led to more effective and equitable talent management aligned with the network's mission and goals.