Prevent eligibility errors before they become federal financial exposure.

Auto.IES is the new AI/NLP-enabled automated eligibility quality assurance (QA) service from Public Consulting Group (PCG) designed to help agencies protect their budgets from the cost of SNAP and Medicaid eligibility errors. Auto.IES allows states to automate the QA of virtually 100% of eligibility determinations – before decisions are made – by instantly identifying potential accuracy and/or procedural errors and returning these issues back to the caseworker to adjudicate before they certify eligibility.

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Why Traditional Eligibility QA is No Longer Enough

State HHS agencies are entering a new era of payment-accuracy accountability and program integrity. Traditional retrospective, sample-based QA methods that identify issues after eligibility decisions have already been made are no longer enough. With new requirements under H.R. 1, agencies need a way to automatically identify accuracy and procedural issues across the full caseload before determinations are finalized. Auto.IES automates this pre-certification visibility through earlier, more consistent case review and policy-aligned analysis on virtually 100% of cases.

Auto.IES is designed to augment—not replace—human judgment, with final decisions remaining under human and agency authority.

Learn how Auto.IES helps agencies address eligibility QA before findings become penalties.

Explore how Auto.IES’s configurable, automated, AI-assisted reviews give caseworkers and program leadership granular visibility into eligibility and procedural issues, documentation gaps, system calculation errors, and policy-application trends across the full caseload.
Explore Automated Eligibility QA
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How Auto.IES Works

Transparent AI. Human-controlled decisions. Audit-ready results.

 

Overview: This graphic presents the Auto.IES end-to-end workflow for reviewing eligibility cases, generating evidence-backed findings, supporting human quality assurance, and producing risk reporting for SNAP QC and Medicaid PERM visibility. Five-stage workflow: The process begins with Case Inputs, including case data, application information, notes and narratives, supporting documents, external data, and activity metadata. AI/NLP Review then analyzes structured and unstructured information, evaluates policy logic and procedural requirements, and generates evidence-backed findings. Findings + Evidence + Confidence provides policy-aligned reasoning, source-linked evidence, confidence indicators, and standardized resolution guidance. Human QA Review uses the same evidence and guidance for reviewer oversight. The final stage, Resolution + Error-Rate Risk Reporting, produces audit-ready documentation, error-rate risk reporting, training insights, and operational improvement. Foundational characteristics: The bottom row emphasizes read-only access to systems of record, configurable thresholds, evidence-backed outputs, and SNAP QC and Medicaid PERM visibility. The image positions Auto.IES as a human-in-the-loop, evidence-driven review approach that improves quality, transparency, and compliance readiness.

See how Auto.IES transforms eligibility review.

Explore the step-by-step process behind policy-aligned case analysis, transparent findings, and human-centered decision support.
See how Auto.IES transforms eligibility review.
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Key Benefits

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Reduce HR-1 financial risk

Identify eligibility issues before they contribute to SNAP error rate or Medicaid PERM exposure.

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Review more cases earlier

Move beyond small QA samples to automated and configurable reviews of your entire caseload.

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Improve policy consistency

Reduce inconsistent rule application across workers, offices, and programs.

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Target training precisely

Identify and address recurring error patterns by screen, rule, office, or worker — not blanket retraining.

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Realize trust and transparency

View every AI step and recommendation that is logged with policy citations.

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Protect sensitive data

  • No data is sent to models over the internet.
  • No models are trained on agency data.

Where Auto.IES Helps Agencies Create Value

Auto.IES helps agencies strengthen eligibility QA accuracy by automating prospective reviews of virtually 100% of eligibility determinations, improving review consistency, identifying potential issues earlier, and supporting documentation for oversight and audit readiness.

  • Automated reviews of virtually 100% of cases
  • Prospective, rather than retroactive QA reviews
  • Granular feedback on eligibility and procedural issues and training needs
  • More consistent QA review across programs and teams
  • Stronger policy citations and audit-ready documentation
  • Better use of staff time and review capacity
  • Dramatic reduction in federal penalties associated with eligibility errors

Learn how agencies implement and scale Auto.IES.

Explore deployment phases, implementation considerations, and best practices for adoption.
Learn How Auto.IES Is Implemented
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Trust, Security & Responsible AI

Designed for regulated HHS environments

 

Designed for regulated HHS environments

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