Government
Audit-grade public-sector decisioning
Deterministic AI for eligibility, benefits, procurement, and regulatory adjudication — built for audit trails that survive FOIA, IG reviews, and legislative oversight.
Government vertical — waitlist open
Target: 2026 — federal & state
Join pilot intake. We prioritize agencies and program offices with a concrete decision workflow that has to survive FOIA, IG review, or legislative oversight — and an AI Governance or algorithmic-accountability framework to ship into.
Public-sector AI has to survive oversight.
Every decision is FOIA-adjacent
Eligibility denials, grant awards, procurement awards, regulatory adjudications — all are discoverable. Probabilistic reasoning with no chain of evidence is an audit liability the moment a journalist or IG files the request.
Algorithmic accountability is statute
OMB Memorandum M-24-10, state-level algorithmic accountability laws, and federal AI Bill of Rights guidance are not aspirations — they are compliance requirements. Deterministic, inspectable AI is the only path that answers them on paper.
Legacy rule-engines are brittle
Existing expert-system workflows are brittle and expensive to maintain. A black-box LLM is not the upgrade. A deterministic AI stack with explicit conflict signals is.
Three QAG workflows on the government roadmap.
Eligibility & benefits QAG
Encode program rules, eligibility criteria, and the applicant record in Q-Prime. Every determination carries the rule, the evidence, and any conflicts — sufficient to defend under hearing review or IG audit.
Procurement & grants QAG
Proposal evaluation reasoned over the solicitation, the proposal, and the compliance envelope. Scoring is a reasoning graph, not a paraphrase — every factor traces to a source clause.
Regulatory adjudication QAG
Case files, policy, and precedent composed on the QAG Engine. Conflict signals surface before the decision memo is written, so enforcement action is replayable and defensible at judicial review.