Private AI for local government
Local governments have enormous opportunities to use AI. They also have entirely legitimate questions about where information goes, how it is processed, how long it is retained and who controls it. We deploy AI systems around those requirements rather than around them.
A published agenda is not a personnel file
Government organizations handle many different types of data, and treating them all the same is the reason so many public-sector AI projects never start.
A city council agenda that was posted publicly last Tuesday is fundamentally different from an internal personnel record. Your AI architecture should understand that distinction — and once it does, most of the objection to the project evaporates, because the sensitive material was never going anywhere in the first place.
Some workloads can remain entirely inside municipal infrastructure. Others can use managed processing. Genuinely public information may be appropriate for selected cloud services.
There does not need to be one rule for everything.
Work with the systems you already have
AI should not require replacing every system you already run — and a proposal that starts with "first, migrate your records system" is not an AI project, it is a migration project wearing a hat.
We build processing services and integrations around existing applications, archives, databases, file systems and workflows. The agenda management system stays. The records system stays. Something useful gets added beside them.
Meetings, then the archive
Almost every public agency we talk to has the same two opportunities sitting in front of it, and they happen to be the two that are easiest to justify.
- ingest recording from the meeting system Runs on your network
- transcribe overnight Runs on your network
- identify speakers against the roster Runs on your network
- captions + summary Runs on your network
- publish to the public portal Runs on your network
- ingest scans decades of them Runs on your network
- OCR the heavy, one-time part Runs on managed
- extract metadata dates, parcels, case numbers Runs on managed
- build index Runs on your network
- public search on your own site Runs on your network
- Runs on your network Runs inside your own environment.
- Runs on managed Runs on infrastructure we operate for you.
- Runs on cloud Uses a commercial cloud or AI service.
Why meetings first
The recordings already exist, nobody enjoys producing minutes from them, captions are often an accessibility obligation anyway, and the entire workload is overnight batch work — which means it can run on hardware the organization very likely already owns.
It is the rare project where the privacy question is easy (a public meeting is public), the value is obvious, and the hardware bill is close to zero.
Why the archive second
A records backlog is the highest-value document project most agencies have and the one they have deferred longest, because OCR across decades of scans is genuinely heavy work.
It is also the textbook case for managed private processing: heavy for a few weeks, then finished forever. Buying hardware for it would mean buying for a peak that never recurs.
More detail on any of it
Cities, towns and villages
What a municipality of any size usually does first, and what it takes to run.
Read moreMeeting transcription
Recordings into transcripts, captions and searchable archives.
Read morePublic records search
Keyword and semantic search across scanned archives.
Read moreOn-premises AI
When the answer is simply "it runs inside our building."
Read moreDeployment options
On-prem, managed private, hybrid and cloud, compared.
Read moreWorkload planner
Four questions, and where your workload probably belongs.
Try itA published agenda is not a personnel file
Public agencies handle both, and the same AI architecture can treat them differently. Start with what you have and where it is allowed to go.