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Citizen services automation
Tier-1 inquiry handling for benefits, permits, licensing, with refusal patterns for out-of-scope queries, multi-language support, and explicit escalation to human caseworkers.
AI & Machine Learning × Government & Public Sector
AI for Government & Public Sector.
Production AI for federal, state, and local government: citizen services automation, document intelligence, eligibility decisioning, and benefits processing. Engineered against equity, transparency, FedRAMP, and FOIA-defensibility frame.
- FedRAMP
- Aligned
- FISMA
- Aware
- Section 508
- Default
- FOIA
- Engineered for
The reality
Public-sector AI fails when it can’t survive procurement, FOIA, or equity scrutiny.
The pattern across public-sector AI engagements: a pilot that worked in lab conditions but couldn't survive procurement; an AI use case stalled by appropriate equity scrutiny that wasn't addressed in design; a document automation rollout that lacked the FOIA-defensible audit trail public-records inquiries require; and an eligibility decisioning model whose explainability didn't meet the standard administrative law actually demands. Public-sector AI succeeds when equity, transparency, and audit defensibility are primary engineering concerns.
Prosigns engages on public-sector AI with explicit awareness of equity, transparency, and FOIA frame. Refusal patterns for high-stakes decisions; equity-aware evaluation built into the eval harness; FOIA-defensible audit logs on every decision; and the operational discipline public-sector AI use must sustain. CITADEL handles regulatory frame; CORTEX handles AI; CANVAS handles citizen-facing accessibility-first design.
Where it ships
6 workloads, scoped to government & public sector.
Concrete applications where ai & machine learning unlocks measurable value inside government & public sector delivery constraints.
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Document intelligence
Document extraction, classification, and analysis for benefits applications, regulatory filings, and case management. Confidence scoring, human-in-the-loop review, full audit lineage.
- 03
Eligibility and benefits decisioning
Decision support (not autonomous decisions) for caseworkers: with explainability, equity-aware evaluation, refusal for ambiguous cases, and the administrative-law defensibility public benefits actually require.
- 04
Public-records FOIA support
Document retrieval, redaction-suggestion AI, and the audit-trail tooling FOIA fulfillment requires. Engineered against the response-timeline statutes public-records laws actually impose.
- 05
Internal operations AI
Procurement document analysis, contract intelligence, regulatory analysis, and the internal automation that frees agency staff for higher-judgment work.
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Multi-language citizen experience
Translation and accessibility AI for citizen-facing surfaces, engineered to WCAG 2.2 AA, plain-language standards, and the multi-language support diverse populations actually need.
How we engage
4 phases, named in the SOW.
Each phase has a deliverable, an owner, and an acceptance criterion calibrated to government & public sector delivery.
- 01
Equity, transparency, and procurement frame
Discovery includes equity threat-model (which decisions, which subgroups, which fairness metrics), procurement reality (FedRAMP, StateRAMP, agency acquisition rules), and FOIA frame (what audit trail does the records statute require). Architecture lands against this frame in writing.
- 02
Refusal-first design for high-stakes decisions
Public-sector AI defaults to refusal where the decision has material individual impact (benefits, eligibility, access to services). Decision support, not autonomous decisions. Human-in-the-loop checkpoints by default; explicit override paths.
- 03
Equity-aware evaluation
Eval datasets stratified across the demographic subgroups relevant to the deployment context (race, ethnicity, sex, age, language, disability). Per-subgroup metrics surfaced rather than averaged. Equity gaps are release-blocking.
- 04
FOIA-defensible operations
Every AI decision logs input, output, model version, retrieved citations, user identity, and the full reasoning chain. Retention per state / federal records statute. Audit pulls measured in days. We engage with the records officer from kickoff.
Capabilities
What’s in scope.
- Citizen services automation with refusal patterns and multi-language
- Document intelligence: extraction, classification, redaction-suggestion
- Decision support for caseworkers with equity-aware evaluation
- Public-records / FOIA support tooling with audit-trail defensibility
- Procurement document and contract analysis
- Multi-language citizen experience with accessibility AI
- FedRAMP-aligned deployment via authorized cloud providers
- Equity-aware eval datasets stratified across demographic subgroups
Stack
Tools we use in production.
- Cloud (FedRAMP)
- AWS GovCloudAzure GovernmentGCP Assured Workloads
- Foundation models
- Anthropic Claude (BAA)AWS BedrockAzure OpenAI
- Document AI
- Custom OCRAWS TextractAzure AI Document IntelligencePaddleOCR
- Eval & audit
- PromptfooCustom equity eval frameworksSplunkAudit Manager
- Identity
- Login.govID.meMicrosoft Entra IDOkta GovCloud
- Languages
- PythonTypeScriptJavaC#
Compliance overlay
Frameworks we engineer to.
Every government & public sector engagement carries the evidence collection that procurement and audit teams expect on day one.
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FedRAMP / StateRAMP
FedRAMP Moderate / High alignment via AWS GovCloud or Azure Government for federal workloads. StateRAMP for state-level agency clients. We partner with FedRAMP-authorized providers; we do not hold our own ATO.
- 02
FISMA
FISMA-aware design with NIST 800-53 control mapping, continuous monitoring, and the audit-evidence pipeline federal IT examination requires.
- 03
Section 508 / WCAG 2.2 AA
Citizen-facing AI engineered to Section 508 conformance from architecture, with assistive-technology testing on every release. Multi-language and plain-language design as defaults.
- 04
FOIA / public records
Audit logs designed for public-records response. Every AI decision captures input, output, model version, retrieved citations, and reasoning chain. Retention per applicable records statute (federal / state / local). We engage records officers from kickoff.
- 05
Equity & fairness
Eval datasets stratified across demographic subgroups. Per-subgroup metrics released alongside aggregate. Disparate-impact testing in CI. Refusal patterns for high-stakes decisions. The subgroups that matter and the fairness criteria are documented in the SOW.
Selected work
Quantified outcomes, not adjectives.
Where this fits
Open the parent practice or industry hub.
Common questions
Asked before the first call.
- 01
Are you FedRAMP authorized?
We engineer to FedRAMP standards where required, and partner with FedRAMP-authorized cloud providers (AWS GovCloud, Azure Government) for hosting rather than holding our own ATO. CITADEL co-pilots ATO support as part of every federal engagement.
- 02
How do you handle equity in public-sector AI?
Equity-aware evaluation is built into the eval harness, not bolted on later. Ground-truth datasets are stratified across the demographic subgroups relevant to the deployment context. Per-subgroup metrics released alongside aggregate. Disparate-impact testing runs in CI. Subgroup gaps are release-blocking, not backlog items.
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Can your AI decisions stand up to administrative review?
Designed for it. Public-sector AI in our portfolio is decision support, not autonomous decisions. Every decision logs input, output, model version, retrieved citations, and the reasoning chain. Explainability documentation produced as a side-effect of building, not retrofitted before review.
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How do you handle FOIA / public records?
We engage records officers from kickoff. Audit logs designed for public-records response with retention per applicable statute. Redaction-suggestion AI for sensitive content. The audit trail surviving FOIA scrutiny is part of the architecture, not bolted on after launch.
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Can you do citizen-facing chatbot AI?
Yes: with refusal patterns for high-stakes queries, multi-language support, accessibility-first interfaces (WCAG 2.2 AA), and explicit escalation to human caseworkers on edge cases. We tell you when a use case is too high-stakes for AI-only handling.
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What does a public-sector AI engagement cost?
Discovery: 6–10 weeks, $80K–$200K. Citizen-services AI build: 9–14 months, $800K–$2M. Document intelligence program: 6–12 months, $500K–$1.5M. Multi-year modernization with AI: $2M–$8M+. Managed Services: $50K–$300K monthly retainer.
Talk to us
Bring us a hard AI & Machine Learning problem. We’ll bring a senior Government & Public Sector engineer.
A senior engineer plus the CORTEX department lead joins the first call, both with prior government & public sector delivery experience.