Governed AI for state, county, and municipal agencies.
Residents cannot choose another provider. That single fact changes what responsible AI adoption means in government — and it is why we start with the decisions that carry legal consequence, not with the tools.
Lowest of 14 sectors measured. McKinsey & Company, Public Sector Practice, Jul 2026.
The public sector starts furthest behind — and has the least room for error.
AI Quotient of the social and public sector — the lowest of 14 sectors measured, against a global average of 33
of public sector employees trust their employer to develop AI safely — against 71% across all industries
of domain-based AI programs reach production, versus programs led by individual use cases
Sourced and dated on our evidence register.
“Agencies pulling ahead design for risk management from the start rather than retrofit governance after deployment.”
Human sign-off by consequence, not by category.
Most agencies default to a human reviewing everything, because nobody has drawn the line. That is not caution — it is an unfunded review burden that stalls the program. We classify each workflow by what happens to a resident when the model is wrong, and wire the sign-off requirement to that, in the platform, per operation.
Records summarization, meeting notes, internal drafting, translation of published material.
311 responses, plain-language rewrites of notices, status explanations, appointment handling.
Benefit eligibility, license and permit decisions, inspection findings, enforcement referrals.
“A benefit denial, a license revocation, or a public safety dispatch is a consequential decision requiring a human in the loop. A grammar check on a draft response is not.”
McKinsey & Company, Public Sector Practice, p.10
What government needs that the private-sector playbook doesn’t cover
An explainable, reviewable, auditable record
A decision affecting an individual has to be defensible later — to the resident, to an appeal, to an inspector general. Every model call in our platform writes a cryptographically signed audit event; the log can be verified for tampering without trusting the system that wrote it.
Inference cost bounded before it ends the program
Per-agent budget envelopes with period rollover, a cost ledger written on every call, and hard stops when an envelope is exhausted. Appropriations do not flex mid-year, so the spend has to be bounded in the system rather than in a spreadsheet.
Procurement that survives contact with the pilot
Free proofs of concept that dazzle in a demo and then have to be rebuilt for scale are the most common way an agency loses a year. We scope for the workflow at volume from the start, and price against it.
Public records and retention, applied to AI output
Model-generated material is a record. Where it lands, how long it is kept, and whether it is disclosable are questions with statutory answers — cheaper to settle before deployment than after a request arrives.
“AI run costs can rise quickly in agencies that don't track them, and many agencies don't. … Building financial operations capabilities around AI early is cheaper than retrofitting them after the bill arrives.”
Where we work, and where we don’t
- +State agencies and departments
- +County and municipal government
- +Public authorities, districts, and transit bodies
- +Public higher education — see For Higher Ed
- −US federal agencies
FedRAMP authorization, FISMA, and the ATO process are a compliance surface we do not cover today, and we would rather say so than have you discover it in month three of an engagement.
Start with the seven-minute Snapshot
Ten dimensions, a maturity score, and the two or three things worth fixing first. No cost, no call required.