Civica

Inspiration

Millions rely on SNAP and related programs, yet the intake and review process remains burdened by manual work—long narratives, scattered documentation, and inconsistent signals about urgency and risk. As AI-assisted applications become faster and more sophisticated, government systems have lagged behind, widening the gap between submission and review. Caseworkers shouldn’t spend their time re-entering data or searching for inconsistencies across forms. Their time is far more valuable when focused on judgment, context, and making fair, informed decisions.

What it does

Civica is an AI-assisted triage layer for SNAP-style public-benefits intake. Every submission lands in an actual caseworker's queue. Caseworkers get a dashboard with workload and priority signals, and can process pending applications in batches. The system generates AI summaries, priority and risk scores, document vs. declared-field checks, fraud and integrity flags, and routing (e.g. auto-ready vs. needs deeper review vs. high risk) for every case.

Reviewers open a single application to see AI summary and advisory recommendation, mismatches, risk breakdown, and extracted document fields with confidence. They can work in an internal form workspace (prefilled eligibility-style flow) and a document review view to verify OCR-style extractions. Caseworkers make the final call—approve, reject, or request follow-up—with notes. Civica is built to accelerate and structure review, not replace human judgment.

How we built it

A React + Vite SPA talks to a Node.js + Express API. Data lives in Supabase (PostgreSQL) for applications and document metadata. File uploads go through the API (Multer), with extracted fields stored for comparison against what applicants declared . Each application runs through a pipeline that combines OpenAI-powered steps (e.g. narrative summary, priority hints, narrative inconsistency checks) with deterministic rules (integrity checks, validation against aggregated document data, scoring, routing, and draft recommendations). We intentionally separate “model output” from auditable flags so reviewers can see why something was flagged.

Challenges we ran into

Trust and UX: Making it obvious that recommendations are advisory and that humans approve or deny.
Latency and cost: Batch triage over many cases means many model calls; we balanced throughput with predictable demo timing and clear loading states.
Polish under pressure: Replacing rough feedback (e.g. blocking alerts) with lighter-weight notifications so demos and recordings feel product-grade.

Accomplishments that we're proud of

A coherent end-to-end story: applicant intake → queue → batch + individual triage → structured review → caseworker decision.
Explainability by design: summaries, mismatches, flags, and document confidence, not just a risk percentage.
Grounded in real casework concerns: urgency, consistency between narrative and fields, documents vs. declarations, and fraud-adjacent signals without claiming certainty we don’t have.

What we learned

Batch changes the product: the value isn’t only faster single reviews, but also ordering a queue when volume spikes (e.g. after a policy change or enrollment drive).
Government-adjacent AI wins when outputs are inspectable (bullets, tables, flags) and when every automated signal has a path to human override.

What's next for Civica

Document-heavy SNAP application processing is the ideal environment to validate the effectiveness of an AI layer for government workflows.
The same core workflow improvements from the pilot use case can be reused in other government applications (e.g. unemployment insurance, housing assistance, etc.).