AI in Government
Public sector AI that fits inside an existing authorization boundary.
Government AI projects rarely fail on capability. They fail on authorization: the model that works sits outside the accreditation boundary, and getting it inside takes longer than the program has. Senteras designs for the boundary first, using open-weight models that can be deployed inside existing accredited infrastructure rather than requiring a new authority to operate for an external service.
Book a free consultationWhat makes this hard
- Most commercial AI services sit outside existing authorization boundaries, and adding one is a multi-quarter process
- Public records law means model inputs and outputs may themselves be disclosable, which changes how you log them
- Procurement cycles are longer than model release cycles, so a specified model is often superseded before contract award
- Constituent-facing errors carry political cost that no efficiency gain offsets
Where AI earns its place
Constituent Service Response
Draft responses to high-volume routine enquiries grounded strictly in published policy, with citations to the source document in every answer.
Backlog reduction on routine correspondenceRecords and FOIA Processing
First-pass responsiveness review and redaction candidate identification across a request population, with every proposed redaction reviewed by an officer.
Weeks off statutory response timelinesPermit and Application Review
Completeness checking against the published requirement set before an application reaches a reviewer, so incomplete submissions are returned in hours rather than weeks.
Fewer review cycles per applicationProcurement and Contract Analysis
Compare submitted bids against solicitation requirements and surface gaps for the evaluation team, without the model scoring or ranking anything.
Evaluation time on substance, not compliance checkingFigures are drawn from Senteras engagements and are illustrative of typical results. Outcomes vary by data quality, infrastructure and scope.
The rules that shape the build
These are the constraints that decide the architecture, usually before anyone has picked a model. This is general information about the regulatory landscape, not legal advice on your obligations.
FedRAMP
Federal use of a cloud service requires an authorization the major AI APIs hold only for specific government offerings. Self-hosting inside an already-authorized environment sidesteps the gap.
CJIS Security Policy
Criminal justice information carries personnel screening and location requirements that effectively rule out general commercial inference.
Public records and sunshine laws
Prompts and outputs relating to public business may be public records. Design the logging so it can be produced, and tell staff that it can.
Open-weight models, inside the existing boundary
Senteras deploys open-weight models on infrastructure that already holds an authority to operate, rather than routing to an external service that would need its own. This is usually the difference between a project that ships in a quarter and one that spends a year in authorization. It also means the agency can change models later without repeating the process.
Where this applies
The same core systems, with the differences that matter in each setting.
- Local and municipal government
- Constituent correspondence and permit intake carry the clearest payback and the least authorization friction.
- Defense contractors
- ITAR and CMMC push everything on-premise; the model choice is constrained by export control, not benchmark scores.
- Public utilities
- Outage communication and work order triage; often regulated at state level rather than federal.
Common questions
Do we need a new authority to operate?
Not if the model runs inside infrastructure that already holds one, which is the main reason we deploy open-weight models rather than routing to an external service. This is frequently the difference between shipping in a quarter and spending a year in authorization.
Are AI prompts and outputs public records?
Where they relate to public business, quite possibly. Design the logging so it can be produced on request, and tell staff that it can. Discovering this during a records request is worse than planning for it.
Can AI make eligibility or benefit determinations?
We do not build it that way. Completeness checking, document assembly and drafting are appropriate. The determination stays with an accountable official, both because due process requires an explainable decision and because the political cost of an automated error is not worth the efficiency.
How do you handle CJIS-regulated data?
Personnel screening and data location requirements effectively rule out general commercial inference, which pushes the deployment on-premise inside the existing CJIS boundary.
How we build it
Local & On-Prem LLM Deployment
The most powerful AI models, running entirely on your hardware.
AI Strategy & Roadmap
A clear path from where you are to where AI can take you.
Custom AI Agents & Automation
AI that doesn't just answer questions. It gets things done.
Document Processing
Extraction, classification and validation across formats you do not control.
Internal Knowledge Base
Answers from your own documents, under your existing permissions.
Customer Service AI
Deflect the routine volume without trapping anyone in a loop.
Start with a conversation, not a proposal
Thirty minutes. We will tell you what we would change first, and whether you need us at all.
Book a callThe firm behind the firm