Benefits Eligibility Screener

Part of the worlds-biggest-software-project initiative.

An open-source, AI-native screener that helps people discover every government, tax, and nonprofit benefit they qualify for in a single conversation.

The Benefits Eligibility Screener is a multi-program eligibility platform for residents, social-service navigators, and public agencies. It combines a Rules-as-Code back end with conversational AI intake to replace static form trees and multi-month policy-update cycles with plain-language interviews and rapidly maintainable rule sets.

Why Benefits Eligibility Screener?

Incumbent enterprise platforms such as IBM Cúram require multi-million-dollar government contracts and weeks-to-months change-management cycles for even minor policy rule updates.
Salesforce Public Sector Solutions delivers modern UX but carries per-user enterprise licensing that is prohibitive for smaller agencies and nonprofits, and rule changes still require Salesforce expertise.
Existing open-source tools (PolicyEngine, MyFriendBen, ACCESS NYC, OpenFisca, Nava OSCER) each solve part of the problem but lack a unified offering that combines cross-program eligibility, AI conversational intake, and document understanding.
Findhelp's strength is community-resource referral rather than deterministic eligibility determination, leaving a gap for tools that compute dollar-value benefits across federal, state, county, and nonprofit programs.
Federal policy volatility in 2025–2026, plus SNAP and Medicaid unwinding, is driving urgent demand for configurable, rapidly updatable rule engines that small agencies and CBOs can actually afford to run.

Key Features

Multi-Program Eligibility Screening

Questionnaire-driven screening covering SNAP, Medicaid/CHIP, SSI, EITC, WIC, LIHEAP, and housing vouchers
Plain-language progressive-disclosure questions that adapt to prior answers
Benefit dollar-value estimation with time-to-apply for each matched program
Cross-program application roadmap that prioritises next steps for the household

Rules-as-Code Engine

Versioned, auditable eligibility logic linked to regulatory sources (Title XIX/XXI, ACA MAGI, federal poverty levels)
YAML/Python-style rule definitions inspired by OpenFisca and PolicyEngine for interoperability
Quarterly update process for poverty lines, benefit thresholds, and program changes
Public REST API so third parties can build their own front ends against the rules engine

Conversational AI Intake

LLM-powered natural-language interview replacing rigid form trees
AI-generated explanations of why a household qualifies (or does not) for each program
Document upload with AI extraction of income, household, and asset data from pay stubs and tax returns
Predictive eligibility scoring from partial data to reduce questionnaire abandonment

Accessibility and Reach

Mobile-first front end meeting WCAG 2.2 / Section 508 / ADA requirements
English and Spanish at launch, with Chinese, Vietnamese, Arabic, Portuguese, and French Creole on the roadmap
Embeddable widget and white-label deployment for community partners and counties
Closed-loop referral integration with community-resource networks

Policy and Outreach Tooling

Cliff-effect visualisation showing how income changes affect benefit eligibility
Policy reform simulation to compare current law against proposed changes
Proactive outreach scoring to identify likely-eligible non-participants from administrative data
Admin UI for non-technical policy analysts to update thresholds without developer intervention

AI-Native Advantage

AI capabilities turn this from a static screener into a living policy system. LLMs translate statute and regulation text into executable Rules-as-Code, cutting update cycles from months to days. Conversational intake replaces form trees with adaptive interviews in plain language, while document-understanding models extract income, household composition, and asset data directly from uploaded pay stubs and tax returns. Machine-learning models also identify likely-eligible non-participants from administrative data to drive proactive enrollment campaigns.

Tech Stack & Deployment

The platform is designed as a Rules-as-Code back end with a public REST API and an accessible USWDS-style front end. Rule definitions follow OpenFisca- and PolicyEngine-compatible formats so existing rule libraries can be imported and exported. Deployment targets cloud-agnostic Infrastructure as Code for AWS, Azure, and GCP, with embeddable widgets and white-label configurations for community partners. Standards alignment includes ACA MAGI rules (45 CFR Part 155), HIPAA, Section 508, and the OECD-led Rules-as-Code framework.

Market Context

US federal and state benefit programs collectively distribute over $2 trillion annually, and the AI in Government and Public Services market is forecast to grow at 16% CAGR through 2035 (InsightAce Analytic, 2026). Government-procured eligibility systems range from hundreds of thousands to tens of millions of dollars, while commercial SaaS screeners run $10,000–$200,000/year. Primary buyers are state Medicaid and SNAP agency directors, county health and human services departments, nonprofit social-service navigators, and health systems operating community benefit programs.

Project Status

This project is in the research and specification phase.
Contributions, feedback, and domain expertise are welcome.

Contributing

We welcome contributions from developers, domain experts, and potential users. See CONTRIBUTING.md for guidelines.

Important: All contributions must be your own original work or clearly attributed open-source material with a compatible licence. Copyright infringement and licence violations will not be tolerated and will result in immediate removal of the offending contribution. If you are unsure whether a piece of code, text, or other material is safe to contribute, open an issue and ask before submitting.

Licence

Licence to be determined. See discussion for context.