Back to Industries

AI for Government & Public Sector

AI for constituent casework, public-records search, benefits eligibility intake, and document review — built for agencies that compete on case-cycle time and constituent trust, not headcount.

Book a 30-minute callSee similar work
≈55%
Case-cycle time reduction
≈4x
Document review throughput
99%+
Citation accuracy (RAG, in-corpus)
8–12 weeks
Time to production

Trusted by teams at MatchWise, ServiceCore, QuantFi, Desson Abogados, Mexico Por el Clima, and others across the US and LATAM.

What we build

Anatomy of an AI workflow for Government & Public Sector

Each ships in 8–12 weeks. Pick a workflow to see what goes in and what comes out.

Constituent casework copilot

Staff open a constituent inquiry and get a grounded summary of every prior interaction, the matching policy or program rules, and a draft response in the agency's voice — with citations back to the case file and policy section.

20–45 min per case lookupUnder 2 min with cited draft

Inputs we read

  • Constituent message (email, web form, 311 ticket)
  • Prior case file and interaction history
  • Program-rule and policy corpus
  • Eligibility and benefits records
  • Agency response templates and tone guides

Outputs delivered

  • Cited case summary with policy references
  • Draft response for caseworker sign-off
  • Recommended next-step routing
  • Risk and escalation flags
  • Per-case audit trail with model version

Decide your path

Build, buy, or partner?

Three real options, each with different trade-offs on cost, control, and customization.

Palantir · Accenture Federal · Microsoft Azure Government

Vendor SaaS

Best for: Generic case management or document review with off-the-shelf workflows

Data control
Vendor-controlled; data may route to vendor LLM
Customization
Low to medium — preset playbooks
Time to value
Months for procurement; days to configure
Cost (3 yr)
High recurring per-seat / per-record fees
Recommended

Clearframe partner build

Best for: Agencies with unusual program rules, multi-system estates, or strict data-residency / FedRAMP-aligned requirements

Data control
Your environment; no third-party training; FedRAMP-aligned hosting
Customization
High — fine-tuned on your policy corpus and case history
Time to value
8–12 weeks
Cost (3 yr)
Predictable; pays back in 90–180 days at agency scale
DIY

In-house build

Best for: Agencies with mature internal data-science and security teams

Data control
Full control
Customization
Full
Time to value
12+ months
Cost (3 yr)
Highest upfront, lowest recurring

What is AI for government and public sector?

AI for government and public sector is the application of natural language processing (NLP), retrieval-augmented generation (RAG), and large language models (LLMs) to the document- and case-heavy work that defines agency throughput — constituent casework, public-records review, benefits eligibility intake, policy analysis, and document review. It does not replace caseworkers, eligibility workers, FOIA reviewers, or policymakers; it removes the lookup, packet-assembly, and dig-time layers that consume staff hours without adding judgment or authority.

Agencies run on documents and case files — constituent inquiries, eligibility paperwork, public-records requests, rulemaking comments, retention-bound correspondence. We build AI that reads, retrieves, and drafts alongside agency staff, so the workflow captures more capacity per staff member without diluting the human-in-the-loop oversight required for consequential decisions. The opportunity at federal scale is unusually large: McKinsey's 2023 productivity analysis estimates roughly $519B in annual U.S. government productivity gain available from generative AI — the largest single-sector opportunity in their study.

Glossary

Key terms on this page

FOIA / public records

Freedom of Information Act (federal) and state-equivalent laws that compel agencies to disclose records on request, with exemptions for privacy, security, and deliberative process.

FedRAMP

Federal Risk and Authorization Management Program — the standardized security framework for cloud services used by U.S. federal agencies.

RAG (Retrieval-Augmented Generation)

A pattern where an LLM answers questions using documents it retrieves from your agency's own corpus, with citations back to source — the architecture that makes generated answers defensible.

PII / SPI

Personally Identifiable Information and Sensitive Personal Information — categories of constituent data that trigger redaction and access-control rules.

Audit trail

Per-action log of inputs, model version, retrieval evidence, reviewer sign-off, and outputs — required for any AI-assisted action subject to public-records or IG review.

How we work

What the engagement looks like

A typical first engagement runs 8 to 12 weeks and ships a single production-grade workflow — usually constituent casework or FOIA review for one bureau. Cooperative agreements and existing IDIQ vehicles work; we are not a prime on most federal contracts.

1–2 weeks

Step 1

Paid scoping sprint

Map the case-management estate, policy corpus, baseline cycle times, and the security boundary (FedRAMP-aligned tenant, on-prem, or a sponsored govcloud account). Agree on success metrics with the program owner and CIO.

System inventoryBaseline metrics per workflowSecurity boundary diagramSuccess criteria
6–8 weeks

Step 2

Build

Same senior engineers from kickoff to deploy. Weekly demos against de-identified samples from your own records — never a synthetic dataset. Privacy officer and program counsel review every iteration.

Weekly demosPolicy-grounded retrievalCitation-first generationAudit-trail wiring
Week 8–12

Step 3

Production deploy

Roll out to one bureau or program office behind a feature flag with staff opt-in. Measure cycle time, reviewer override rate, and constituent outcomes before expanding agency-wide.

Feature-flag rolloutBureau-level pilotLive access monitoring

We don't ship demos. Every deployment is measured against case-cycle time, reviewer override rate, FOIA backlog clearance, and constituent-facing response quality.

How we handle your data

Constituent data stays inside your environment — no third-party model training, no data routed to external LLMs, no PII in logs — with structured audit trails on every model decision so privacy officers, inspectors general, and public-records reviewers can sample any output and trace it to source.

What we do

Constituent data stays in your environment
No third-party model training on agency data
Per-caseworker and per-record access logs
Human reviewer signs every consequential output
Source-citation audit trail per generated answer

Architectures designed to meet

FedRAMP Moderate / High control baselines
FISMA & NIST SP 800-53 controls
NIST AI Risk Management Framework (AI RMF 1.0)
OMB M-24-10 federal AI guidance
State public-records and privacy laws

We don't carry these certifications ourselves — your firm's compliance posture stays yours to claim.

Frequently asked questions about AI for government & public sector

Do you hold FedRAMP authorization, and can you work with federal agencies?
We do not carry our own FedRAMP authorization — we build inside FedRAMP-authorized environments (AWS GovCloud, Azure Government, Google Public Sector) and on existing agency tenants. Most engagements run on the agency's own cloud account or on a sponsored govcloud tenant we configure to the relevant FedRAMP Moderate or High control baseline. We work with federal agencies most often through cooperative agreements, state/local programs, or as a subcontractor on existing IDIQ vehicles — we are not a prime on standalone federal contracts.
How do you keep AI outputs auditable for inspector general and public-records review?
Every AI-assisted action carries a structured audit trail: input data, retrieval evidence, model version, retrieval sources cited, human reviewer, and timestamps. Privacy officers and inspectors general can sample any case decision or generated response and trace each claim back to source in seconds. Because the system uses retrieval-augmented generation grounded in your policy and case corpus rather than free-form LLM output, the citation rate on factual claims is 99%+ on in-corpus content — every generated answer points to the document or record it came from.
How does the AI handle PII redaction in FOIA and public-records review?
The model proposes redactions against the disclosure-exemption rules — PII categories, deliberative process, law-enforcement carve-outs, security-sensitive material — and a human reviewer approves or overrides every proposal before disclosure. The AI never decides what is released. Reviewers see the model's rationale per redaction and can override with a one-line note that feeds back into the precedent corpus, so the system improves on your agency's actual disclosure decisions rather than a generic exemption library.
Will this be used to deny benefits or make adverse decisions about constituents?
No. We do not build systems that autonomously grant or deny benefits, eligibility, or any adverse action. The AI assembles complete files, surfaces missing documents, drafts language, and routes work to eligibility workers and caseworkers — humans make every consequential determination. This boundary is hard-coded into the workflow: the system has no auto-approve or auto-deny path on benefits, eligibility, or adjudication. The OMB AI guidance and NIST AI RMF both call this out as a high-risk category, and our deployments are scoped to stay on the assistive side of that line.
What happens to constituent data, and is anything used to train models?
Constituent data stays inside your environment — your cloud tenant, your access controls, your data residency. No constituent data is routed to a third-party model provider for training, and our contracts prohibit any vendor in the stack from training on agency data. Logs are scrubbed of PII before they leave the analysis tenant. We can deploy fully on-prem for agencies with air-gapped requirements; cloud-tenant deployments are the more common pattern and are configured to FedRAMP Moderate or High controls depending on the data classification.
How do you handle bias and disparate impact in constituent-facing AI?
Every deployment includes a bias-audit step against the demographic categories relevant to the workflow — for benefits intake, that is the protected classes in the program's authorizing statute; for casework, the constituent demographic mix in the bureau. We measure model behavior across those groups on real (de-identified) agency data before production and continuously after deployment. The NIST AI RMF's measure-and-manage functions are wired into the deployment as live dashboards, not a one-time report, so program owners and civil-rights officers can see drift as it happens.
How long until ROI on the first agency rollout?
First-bureau ROI usually lands in 90–180 days at agency scale. Constituent casework and FOIA review produce the fastest payback because both compress staff hours on document-heavy work without changing the underlying policy or decision authority. We measure against baselines captured during the scoping sprint — case-cycle time, FOIA backlog, response-quality scores — so the ROI calculation is grounded in your own numbers rather than a vendor case study.

Related services

retrieval augmented generationnatural language processingworkflow automationinternal toolingdata engineeringai machine learning

Most government & public sector teams we work with ship to production in 90 days.

Worth 30 minutes to see what that would look like for your firm? Book a call with one of our senior engineers — no sales handoff, no deck.

Book a 30-minute call