APPLIED AI FOR THE PUBLIC GOOD
Real-world AI for real-world problems.
For public-sector and institutional decision-makers evaluating AI under real deadlines, regulations, and public accountability. RealWorldAI.ai documents applied AI work across pilots, in-development systems, and deployed projects, with implementation stage, limitations, and human responsibility kept in view.
Human-in-the-loop
A human makes the consequential call.
Explainable
Every recommendation can show its reasoning.
Auditable
Every system can be inspected after the fact.
Start with your role
Evidence for decisions. Practical context for builders.
Public-sector and institutional decision-makers
Review implementation context, human-control boundaries, and limitations before deciding whether a project merits a deeper institutional conversation.
Ecosystem partners and practitioners
Explore field patterns, evidence standards, and responsible-AI questions that can inform implementation work elsewhere.
How we build
Built from inside the roles it serves.
Five working rules, learned from inside a commission seat, a coastal watershed, and a tech-transfer pipeline.
What we do
Built for the settings where “just trust the model” doesn’t fly.
Case studies
Applied AI in real settings.
These pages document applied-AI work at its stated stage: pilot, in development, or deployed. Written from inside the ecosystem, they describe what was tested, what remains unfinished, and what stays with a human decision-maker. AI can look convincing in a demo and behave differently when it meets a parcel map, a permit deadline, or a public-records request. The featured work spans coastal stormwater grants, special-district operations, and a university's path from patent to license. It's all first-party, from the network mapped at dougliles.com/ecosystem.
Most AI looks great on stage and breaks when it meets a real deadline. The useful question is what has been tested, what remains in development, and what people still decide.