Applied AI
The Applied AI team researches how foundation models, agentic systems, and machine learning change the way buildings are designed, built, operated, and stewarded.
What we research
The Applied AI team researches how foundation models, agentic systems, and machine learning change the way buildings are designed, built, operated, and stewarded — and how the architects, engineers, and specialists responsible for them can integrate these tools without surrendering the professional judgment buildings require.
Our research focuses on safety and accountability. AI systems in the built environment are being asked to draft, monitor, remember, and recommend. They are not being asked to seal, file, or decide. The team works on how that line holds in practice, how licensed professionals remain accountable for the work, and how the tooling reflects that accountability.
Our first publication, Machine-Readable Buildings, describes how AI accelerates the circular economy in New York and what the same pattern means everywhere else.
Current focus areas
- Agent-assisted compliance workflows for facade, energy, and landmark regimes
- AI safety inside licensed professional practice
- Foundation-model reasoning across building-domain data (codes, filings, drawings)
- Evaluation methods for AI systems used in the built environment
Team lead
Jeremy Edwards — Founder and Director of Research.