About

Matt Hartigan is an Annotation Systems Architect currently referring to himself in the third person. His work focuses on the operating systems behind AI data production: how annotation, review, evaluation, and feedback workflows transform expert decisions into defensible ground truth and effective model supervision. His work sits at the intersection of annotation governance, data-centric AI, model evaluation, human-in-the-loop systems, data quality strategy, AI infrastructure, and the emerging discipline of supervision system design.

Matt’s background spans product operations, AI data operations, annotation systems, model evaluation, and cross-functional delivery across autonomous vehicles, generative AI, NLP , computer vision, and regulated financial services. He developed his annotation systems architecture practice after building the workflows, metrics, roadmaps, dashboards, vendor models, and operating structures that turn ambiguous AI data needs into production-ready systems.

Matt helps AI and data teams diagnose where annotation, review, evaluation, and feedback workflows lose signal. He translates that diagnosis into practical operating improvements across ground truth formation, quality metrics, benchmarking, workload planning, sampling, training, readiness, routing, escalation.

Research Framework

This consulting work draws from Governed Annotation Systems, an unpublished research framework Matt is developing around interpretive AI data work. The framework examines how institutions produce, measure, review, route, train, and forecast annotation systems when quality depends on judgment rather than labels alone. The central thesis is public: AI data production requires stronger systems for turning expert decisions into defensible model supervision. Deeper materials remain reserved for consulting, advisory, partnership, or appropriate review discussions.