Our approach to statistical analysis

Key Takeaways

• We deliver one-off reports for specific questions, but build automated pipelines for ongoing insights
• Unlike traditional consulting firms that deliver PowerPoints, we create end-to-end solutions at scale
• Our models are transparent and interpretable, built alongside your operational understanding
• We use Bayesian methods to provide full uncertainty distributions, not just point estimates
• You make better decisions with quantified uncertainty, understanding the full range of possibilities

Every organization faces different analytical needs at different stages. Sometimes you just need a one-off report—an ad-hoc analysis to answer a specific business question, investigate an anomaly, or test a hypothesis. These targeted analyses inform strategic decisions without requiring ongoing infrastructure. We deliver clear insights quickly, precisely scoped to the problem at hand.

But most organizations soon discover that they need daily or weekly insights. Regular reporting keeps teams aligned on key metrics, helps spot patterns early, and catches issues before they escalate. The challenge? Manual reporting becomes unsustainable as data volume grows and stakeholders demand more sophisticated analyses. What starts as a simple weekly email quickly evolves into hours of data wrangling that takes analysts away from actual analysis.

This is where we diverge from traditional consulting firms. Rather than delivering another PowerPoint deck or one-off statistical report, we build the analytics pipeline to automate these insights. Our end-to-end solutions transform raw data into actionable reports on your schedule, with automated quality checks that alert you to issues immediately. You get consistent, reliable insights without manual intervention, freeing your team to focus on what matters: making better decisions.

Not only do we believe that automation drives innovation, we believe that statistical modeling shouldn't be a black box. The status quo is to provide an opaque machine learning model for any complicated data question. We take a more rigorous approach.

Instead of black box solutions, we build models side by side with your operational understanding so that our approach fits exactly to how your operation functions. Every parameter is understandable and interpretable. Because we take a Bayesian approach, we provide more than just a point estimate—we deliver a full distribution of potential outcomes. This means you can make decisions under quantified uncertainty, understanding not just what's likely to happen, but the full range of possibilities and their probabilities.