Guided self-service
Multilingual assistance that helps citizens apply correctly the first time.
Government · Case Study
Citizen Services Automation
Automation and assistive AI for case intake, status updates, and guided citizen self-service.
- 45%caseworker workload reduction
- -52%average wait time for citizens
- 100%of decisions transparent and appealable
Delivered
What we built
Streamlined citizen services and reduced workload by 45%.
Case assist
Document validation, evidence summaries and draft determinations for staff review.
Policy grounding
Every answer cited to the governing statute or rule.
Transparent audit
Complete decision trails supporting appeals and oversight.
Inside the build
Half the wait, all the accountability
A state agency's benefit backlog was measured in months. Automation had to cut it while surviving legislative scrutiny — both happened.
The challenge
Backlogs hurting the people least able to wait
Applications queued for weeks; a third arrived incomplete and cycled back for correction. Caseworkers spent their days on document checking rather than the judgment their training was for.
- Multi-week queues on benefit applications
- ~33% of applications incomplete on arrival
- Staff time consumed by mechanical checks
Invoice exception workflowRunning
✓Extract invoice fields1.1s
✓Match against PO0.6s
Resolve mismatch with agentrunning
·Post to ERPqueued
The solution
Help at the front door, assist at the desk
Citizen-facing guidance in six languages cuts incomplete submissions; behind the desk, agents validate documents, assemble eligibility evidence and draft determinations — every case decided by an authorized human, every decision cited to rule.
- Application guidance halving error rates
- Evidence summaries mapped to eligibility criteria
- Draft determinations with statutory citations
Summarize open claims above $50k and draft escalation emails.
query_claims_db
draft_email
✓ policy_check
Found 12 claims. 3 need escalation — drafts are ready for your review.
Human approval requiredReview →
The impact
Faster service the oversight committee endorsed
Wait times fell 52% and caseworker capacity effectively grew by half. When the legislature reviewed the program, the agency demonstrated every decision's trail — the audit became an endorsement.
- 52% wait-time reduction in two quarters
- 45% caseworker workload relief
- Legislative review passed on decision transparency
✓PII detected & redactedenforced
✓Answer grounded in sourcespassed
✓Role-based data accessenforced
✕Out-of-scope tool callblocked
SOC 2GDPRAudit logHITL
Outcomes
What changed for citizens and staff
Citizen wait times
Determinations in days instead of weeks.
Application quality
First-time completeness up dramatically.
Staff morale
Judgment work replacing document drudgery.
Language equity
Six languages served identically well.
Appeal fairness
Complete records making appeals faster and fairer.
Program expansion
Blueprint extending to two more benefit programs.
Delivery
From challenge to outcome
We publish the architecture decisions, delivery approach and measurable results — so you can see how similar outcomes could apply to your enterprise.
- Measurable KPIs
- Architecture transparency
- Cross-industry patterns
- Repeatable playbooks
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1
Challenge
Business pressure, legacy process and constraints.
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2
Solution
Architecture, agents and integration design.
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3
Delivery
Build, evaluate, secure and launch.
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4
Impact
Efficiency, cost, quality or revenue outcomes.
We cut the wait in half, and when oversight asked how decisions were made, we showed them — every case, every rule applied, every human sign-off. The hearing ended early.
Under the hood
The stack behind the program
Deployed in GovCloud with the state's identity and records systems.
- AWS GovCloud
- Anthropic Claude
- Salesforce Gov Cloud
- Postgres
- Elasticsearch
- Okta
- Terraform
- Kubernetes
- Datadog
- Presidio
FAQ
Common questions
Does AI decide who gets benefits?
No — authorized staff decide every case. AI validates documents, assembles evidence and drafts; the human determination requirement is structural and logged.
How was fairness verified?
Disparate-impact testing across demographic groups ran before launch and continues quarterly, with results reported to oversight. Thresholds trigger human review of the affected flows.
Could other agencies replicate this?
Yes — the pattern (guided intake, evidence assembly, human determination, full audit) transfers across benefit, permit and licensing programs.
Next step
Serve more citizens, defensibly
Bring your backlog numbers — we'll scope what accountable automation can absorb.