Solution Methodology

DoD prototype solution catalog with policy-grounded model design.

Each solution tile should act like a miniature product: select source candidates, choose model mode, run analysis, inspect diagnostics, see feature impact, and produce an operational action.

Research-backed

Solution design principles

The gallery should combine research content, data selection, model diagnostics, and action queues.

  • Budget solution: aligns OMB A-11, DoD exhibit structure, FMR budget execution, and FY variance explainability.
  • Anomaly solution: combines negative obligation, missing metadata, concentration, source drift, and amount outlier signals.
  • Audit solution: turns A-123/Green Book concepts into control readiness, evidence sufficiency, and CAP workflow.
  • FinOps solution: profiles recipient concentration, agency portfolio, action month, object class, and obligation lifecycle risk.
  • Document solution: ranks PDF/XLSX/JSON candidates for extraction, snippets, table normalization, and grounded Q&A.
  • Lineage solution: scores parser coverage, refresh risk, domain coverage, and database migration readiness.

What good model output should include

For a credible federal finance prototype, model output must be explainable and auditable.

  • Diagnostics: training rows, source count, confidence, validation approach, and lift/segmentation result.
  • Feature impact: ranked drivers such as account, scenario, recipient, document type, parser status, or control risk.
  • Scored outputs: entity, score, value, evidence, and next action.
  • Recommendations: operationally specific steps that an analyst, auditor, or data engineer can actually perform.
  • Governance: model mode, selected data sources, target, horizon, source signature, and generated timestamp.

Analysis Launcher

AI budget analyst

Dataset Candidates

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