Research lab
State Capacity AI builds guides for using AI in government and public services.
We focus on how decisions get routed, who stays accountable, and what happens when machines and humans make choices together.
State Capacity AI is a project of Occupant. Applied research and tools at occupant.ee.
Public Capacity Lab is our research partner. PCL researches how systems deliver services, how citizens experience those services, and how trust and legitimacy are sustained in civic life.
Contact: ron@statecapacity.ai
What we work on
Decision architecture
Standards and models for how automated systems make decisions inside institutions.
When a system approves a permit, denies a benefit, or allocates funding — what governs that action, who can review it, and how does it get appealed?
We write the specs for decision-making that remains accountable even when machines are involved.
Transparency infrastructure
Tools and patterns that make institutional behavior visible to the people inside and outside the system.
- Disclosure and provenance tools
- Procurement benchmarks
- Audit-ready decision logs
- Frameworks for tracing how money and authority move
If a system affects your life, you should be able to see how it works — and if it spends public money, where it went.
Legibility is a precondition for trust.
Protection frameworks
Structural safeguards that prevent systems built for public good from being repurposed for extraction, abuse, or opacity.
- Licensing and governance models
- Anti-capture design patterns
- Audit and oversight structures
- Research into failure modes, evasion, and institutional blind spots
Open systems need protection. So do the people operating inside them.
Published guides
How AI agents should work in government services.
Templates for telling people when AI is part of a decision.
A way to sort AI systems by how much authority they have.
Checklists for using AI responsibly.
A license that adds protections when your work is at risk of misuse.
No guides match your search.
Tools
Field work
We study where public systems break down — and sometimes step in to make those problems legible enough to fix.
Current areas:
- AI and administrative complexity
- Tax and funding opacity
- Decision routing in automated systems
- Procurement and vendor risk
- Institutional fraud and evasion patterns
- Interoperability across jurisdictions
Why this work exists
Decisions in public systems are happening faster than institutions can understand them. The risk isn’t bad outcomes — it’s losing the ability to see where decisions come from at all.
State Capacity AI builds the trust plumbing for that era — so institutions stay governable, and the public can still contest what affects them.
About the lab →