APPLIED AI FOR THE PUBLIC GOOD
Real-world AI for real-world problems.
Most AI looks great on stage and falls apart the moment it meets a real deadline, a real regulation, or a real public-records request. RealWorldAI.ai publishes what actually ships when AI leaves the demo and goes to work on civic, environmental, and institutional problems — built to be auditable, explainable, and useful to the people it claims to serve.
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.
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
What shipped when AI left the demo.
These are case studies in the literal sense: real applied-AI builds from inside the ecosystem, written up honestly — including what was hard and what we left for a human to decide. Most AI demos look great on stage and fall apart the moment they meet a parcel map, a permit deadline, or a public-records request. The work below is what shipped when AI left the demo and met the real world — a coastal county's stormwater grants, the operations of a special district, and a university's path from patent to license. It's all first-party, from the network mapped at dougliles.com/ecosystem.
Podcast
Start here: Intro to RealWorldAI.
An episode of AI for Good: Transforming Communities.
Intro to RealWorldAI
Episode embed goes here — To confirm before launch: confirm: embed URL/iframe
Most AI looks great on stage and breaks the moment it meets a real deadline. This is what shipped anyway.