Constituent services
Answering enquiries from policy, regulation, and prior guidance with citations, so staff verify rather than trust — and so answers stay consistent between offices.
AI for Government & Public Sector
Architectures that satisfy authorization, records law, and the reasonable expectation that public decisions can be explained.
The constraint
Accountability is the requirement
Government AI carries a constraint the private sector does not: the public has a right to understand how decisions affecting them were made.
Every output may become a record. Retention, discovery, and public records requests all attach. An architecture that cannot reproduce what the system saw and produced creates a legal exposure rather than an efficiency gain.
Authorization comes before deployment, not after. FedRAMP, StateRAMP, and agency ATO processes must be planned into the timeline. Retrofitting an authorization boundary around a working pilot is materially harder than designing to one.
Procurement shapes what is buildable. The architecture has to be describable in a solicitation and defensible in a protest, which favours documented, vendor-neutral designs over proprietary stacks.
Applications
Where AI creates value in a public agency
Case processing
Assembling evidence, checking completeness, and surfacing precedent for benefits, permitting, and licensing determinations. Human decision, assisted preparation.
Records retrieval
Making decades of unstructured records searchable for staff and for public records responses, respecting the classification and redaction rules already in force.
Policy analysis
Cross-referencing statute, regulation, and guidance to surface conflicts and dependencies across a large corpus.
Grants and procurement review
Structured comparison of applications or bids against published criteria, with a documented basis for each finding.
Compliance
Authorization and compliance
| Framework | What it drives in the architecture |
|---|---|
| FedRAMP | Where cloud is used, the authorization boundary and control inheritance must be established before design. Private deployment inside an existing boundary is frequently the faster path. |
| StateRAMP | The state and local analogue, increasingly required and broadly aligned with FedRAMP expectations. |
| FISMA / NIST SP 800-53 | The underlying federal control baseline. AI systems are in scope like any other information system. |
| CJIS | Applies to criminal justice information, with specific personnel, encryption, and audit requirements that rule out most commercial AI services. |
| Section 508 | Public-facing output must be accessible. This constrains interface design and document generation, not just the website. |
| Records retention law | Federal, state, and local schedules apply to AI-generated content and to the prompts that produced it. |
We align AI governance to NIST AI RMF and ISO/IEC 42001 alongside your sector-specific obligations.
Integration
Integration with agency systems
Public sector estates are dominated by long-lived systems: legacy case management, records management platforms, document repositories, and mainframe-backed systems of record. We integrate through the interfaces that actually exist rather than assuming modern APIs, and we deploy inside your existing authorization boundary so the ATO path is an extension rather than a new submission.
Questions
Frequently asked
Can this be deployed inside an existing ATO boundary?
That is usually the fastest route. Deploying within an already-authorized environment turns the work into a significant change rather than a new authorization, which can save many months.
How do we handle public records requests for AI output?
Log inputs, retrieved context, model version, and output as records from the start. Retrofitting that after a request arrives is the expensive path.
Does AI make decisions about constituents?
We design these systems to prepare and evidence decisions, not to make them. For determinations affecting individuals, a human decision-maker with a documented basis is both the right design and, increasingly, the legal requirement.
Related
Private AI
Production AI systems running entirely inside your infrastructure, with no third-party processor in the data path.
Private AIDeployment Models
Managed API, private cloud, on-premise, edge, and air-gapped compared — including where each one fails.
Compare modelsDiscuss your constraints.
The assessment evaluates your data, infrastructure, and regulatory position, then recommends a deployment architecture your review process can actually approve.
We will sign your NDA before a detailed technical discussion.