Government AI that survives
the IG, the OIG and the regulator

Citizen RAG

Q&A over agency forms, regulations and benefits guidance.

• Citation back to source paragraph
• Plain-language explanations
• Multi-language (EN / HE / RU)
• Escalation paths to human caseworker

FOIA Response Automation

Document discovery + redaction-suggestion for FOIA requests.

• PII / exemption suggestion (b1 / b6 / b7)
• Audit trail per request
• Reviewer-in-the-loop
• SLA tracking

Document Understanding

OCR + extraction over forms, statutes, case files.

• Form-field extraction with confidence
• Statute / regulation citation graph
• Multi-page table parsing
• Handwriting + low-quality scan support

Public-Sector Fraud Detection

Benefits fraud, procurement fraud, identity fraud.

• Cross-program signal fusion
• Network / graph analysis
• Explainable risk scores
• Investigator workflow integration

Security and Authority to Operate

Built for FISMA / FedRAMP-aware design and ATO process.

• NIST 800-53 control mapping
• Continuous monitoring (CDM)
• Audit trail for every model decision
• Vendor risk management

Model Governance

Documentation, monitoring and audit per NIST AI RMF.

• Model card + system card per Mitchell et al. 2019
• Drift detection on population shift
• Bias monitoring by demographic
• Reportable adverse-event tracking

US: NIST AI RMF, OMB M-24-10, FedRAMP, FOIA

NIST AI Risk Management Framework, OMB M-24-10 governance guidance, FedRAMP-aware design for cloud workloads, FOIA response obligations, FISMA security baselines.

EU: EU AI Act (public sector), GDPR, eIDAS

EU AI Act Annex III high-risk classification for public-administration AI, GDPR Articles 22 + 35 (DPIA), eIDAS for trusted citizen authentication.

IL: Privacy Protection, INCD, GovCloud

Israel Privacy Protection Law 5741, INCD security baselines for public-sector cloud, MoF GovCloud guidance, Freedom of Information Law 5758.

Citizen RAG accuracy

What the industry reports: RAG over agency guidance with retrieval-then-generate reports 85-90% answer-with-correct-citation, vs ~70% for plain LLM (US GAO AI assessment 2024).

Where we come in: Citation back to source paragraph, plain-language explanations, escalation paths to human caseworker, multi-language - so every citizen answer is traceable.

• Citation back to source paragraph (no answer without source)
• Plain-language explanations (no jargon)
• Multi-language (EN / HE / RU)
• Escalation paths to human caseworker

FOIA response time

What the industry reports: Document-understanding + redaction-suggestion reduces FOIA response cycle by 35-50% while preserving exemption accuracy (NARA / OGIS 2024).

Where we come in: PII / exemption suggestion (b1 / b6 / b7), audit trail per request, reviewer-in-the-loop, SLA tracking - reviewer stays in control of every redaction.

• Exemption suggestion (b1 / b6 / b7) with confidence
• Audit trail per request
• Reviewer-in-the-loop on every redaction
• SLA tracking with alerting

Public-sector fraud detection

What the industry reports: Cross-program signal fusion identifies 2-4x more fraud rings than single-program rule-based detection while reducing false-positives by 25-40% (US Treasury OIG 2024).

Where we come in: Cross-program signal fusion, network / graph analysis, explainable risk scores, investigator workflow integration - actionable, not just alert volume.

• Cross-program signal fusion (not single program)
• Network / graph analysis for rings
• Explainable risk scores (SHAP)
• Investigator workflow integration