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Decision Pipelines™ is the infrastructure that turns each consequential federal decision — award, denial, eligibility, recertification, payment authorization — into a structured, append-only record: named owner, rationale, evidence, and outcome, captured at the moment the decision is made. The Decision Semantics™ Engine is the discipline the pipeline makes possible — decisions that are owned, timely, reconstructable, and linked to the outcomes they produce. FraudGuard™ — the first application built on the pipeline — screens every grant and entitlement award against the federal record before disbursement, not after.

Eligibility systems check the rules. Payment systems move the money. Audit finds the loss afterward. None capture the award decision itself — the reasoning and evidence behind it. That gap is where improper payments and fraud live. In FY2025, federal agencies estimated about $186 billion in improper payments across 64 programs — up $24 billion from the prior year; cumulative since FY2003, roughly $3 trillion. GAO separately estimates direct annual fraud losses to the government at $233–521 billion.

The pipeline captures every consequential federal decision in five steps — Signal, Owner, Decision, Action, Outcome — and screens awards at the decision against the federal sources oversight already uses: Treasury Do Not Pay, SAM.gov, HHS LEIE, SSA DMF, USAspending.gov. A hit holds the award on the record with rationale attached — caught at the decision, not recovered cents-on-the-dollar later.

Anchored in federal oversight expectations — the Payment Integrity Information Act (2019), OMB Circular A-123 Appendix C, FAR 1.602 and delegation orders, NIST 800-53 (AU family) audit logging, Privacy Act / SORN, FIPS 140-3, CAC/PIV, Section 508 — and designed for FedRAMP. Built for legislative oversight: prevention over recovery, a reconstructable record committees can use, accountability that follows the dollar down the pass-through chain, aligned with the data-driven detection build-out Congress has urged.

  • AudienceMembers of Congress · Oversight staff · OIG · GAO
  • Pages1
  • PublishedJune 2026
  • FormatPDF