AI Enablement · Data Science · Scientific AI
Bryson Bonham
AI model evaluation, governance, and deployment for enterprise AI
Machine learning · deep learning · language · data science & scientific AI
Bryson Bonham builds AI for regulated production environments. Evaluation, governance, and deployment operate as one system across machine learning, deep learning, and language.
Evaluation systems remain valid after launch: LLM-as-a-judge workflows, drift monitoring, and acceptance criteria tied to business decisions rather than demo accuracy.
Governance is embedded in the architecture of each use case, including clinical guardrails and responsible AI controls, so compliance is structural rather than a post-deployment audit.
Agentic and scientific AI move from proof of concept to production in healthcare and life sciences, including formulation platforms, voice and clinical systems, and environments where failure carries downstream cost.
Focus
Most enterprise AI fails because organizations treat the model as the product. The product is the use case: scoped, measured, governed, then deployed.
That pattern repeats across consulting, healthtech, and platform work. Designed & Measured AI documents the discipline; production work focuses on the evaluation and deployment systems required to sustain it after launch.
Full bio →Background
Bryson Bonham leads AI enablement and data science at Ellipsis Health, building agentic voice AI and clinical intelligence in a regulated environment. Previously, he was Principal Product Lead for McKinsey's Global Scientific AI portfolio, leading teams on formulation AI, indication discovery, clinical trial acceleration, and biomedical literature extraction.
MS Analytics (NC State IAA), BS Biochemistry (Virginia Tech). Cambridge Centre for AI in Medicine. INFORMS Certified Analytics Professional (aCAP).