About Driggs Labs
Driggs Labs is a modern data, AI, and research consulting studio helping teams turn complex, messy information into clear systems, tools, and decisions. We design and build end-to-end solutions across data and systems engineering, application development, AI/ML systems, analytics and dashboards, decision design, and applied research and consulting.
Who You’ll Work With
Trevor Mattos, Founder & Principal Consultant
I started Driggs Labs to help organizations build systems that put rigorous analysis behind real decisions. I’m drawn to technical work that changes something for the better, whether that’s a clearer path for a business or a smarter policy decision.
My early work centered on public policy and economic research, leading projects at Boston Indicators and UMass Boston’s Gaston Institute. Then I pivoted to tech, focusing on analytics and data engineering, building the pipelines, reporting, and dashboards behind customer and product analytics. My work has spanned private sector, nonprofit, and government, and the through line is the same: evidence that holds up, built so someone can act on it.
At Driggs Labs, my approach starts with the questions you need answered, not the tech stack. I dig up the data across platforms and disparate sources, bring it into a cloud warehouse, and build intelligence and reporting layers on top, integrating AI tools where it’s helpful. I start with the smallest version that works, because it’s outcomes we are after, not more complicated tools that nobody uses.
If you are interested in using your data to measurably improve impact or outcomes, let’s talk.
Principles of Practice
1. Clarity before complexity
Every system starts with the decision it must support. If it doesn’t improve clarity or action, it doesn’t ship.
2. End-to-end ownership
We design across the full pipeline — from raw data to final insight — ensuring nothing is lost in translation.
3. Build for use, not demonstration
Tools must live inside real workflows. If no one uses it, it doesn’t matter how elegant it is.
4. Iterative precision
We ship early, refine continuously, and treat systems as evolving artifacts rather than finished products.
5. Opinionated simplicity
We prefer fewer moving parts, explicit decisions, and clean architecture over unnecessary flexibility.