Methodology
AI-Expert Reconciliation. Audit-defensible by design.
HSG's methodology pairs a Large Language Model layer with deterministic statistical and ML models, governed by HSG's signature AI / Expert Reconciliation Log — a complete audit trail recording every AI-suggested action, the deterministic input lineage, the expert reviewer disposition, and the final action of record.
Discovery
Map current-state workflow with telemetry on cycle time, rework rate, and bottleneck stations. Identify automation candidates and quantify potential cycle-time / quality lift. Produce a current-state value-stream map and a tiered automation backlog.
Instrumentation
Tag every input source, transformation step, and output artifact with USSGL / TAS / cohort lineage. Build the data dictionary and metric definitions before automation. Stand up baseline metrics so improvement is measurable.
AI-Expert Workflow Build
LLM (Anthropic Claude API or Azure OpenAI in federal-cloud-eligible regions) drafts the mechanical 80% — input prep, anomaly flagging, certification-sheet auto-population, memo drafting. Expert reviewer confirms or edits every consequential output. Federal-grade audit logging on every action.
AI / Expert Reconciliation Log
HSG's signature transparency deliverable. Every AI-suggested action is paired with the expert reviewer's disposition, timestamp, and the underlying input lineage. The Log is the audit-defense artifact for OIG, GAO, IPA, and A-123 internal-control review.
Federal AI Governance
Aligned to OMB M-25-21 (Accelerating Federal Use of AI), M-25-22 (Driving Efficient Acquisition of AI in Government), Executive Order 14179, and NIST AI Risk Management Framework. Documented model cards, use-case registration, and human-on-the-loop discipline throughout.
RD-Maintainable Handoff
Every workflow ships with a 'RD-maintainable' tier — Power Automate, Excel, Python notebooks the OBP can maintain in-house — plus an SOP and a quarterly health-check protocol delivered as both written documentation and a video walk-through.
Why This Methodology Wins
The 80/20 split that protects RD's audit posture while shrinking cycle time.
Federal financial work has two layers: the mechanical 80% (data pulls, reconciliations, certification-sheet generation, memo-template population) and the judgment 20% (model design, OMB negotiation, audit defense, policy interpretation). AI without expert review is reckless on the 20%. Expert work without AI on the 80% is wasteful in a 32%-FTE-cut world.
HSG's methodology lets the LLM draft the 80% and the federal-grade expert reviewer confirm every consequential action. The AI / Expert Reconciliation Log captures the full audit trail. The result: cycle-time compression on routine work, expert capacity protected for the hard work, and an audit-ready posture that holds up under OIG, GAO, and A-123 review.
This is not a theoretical methodology. We've applied it on USDA APHIS overseas workforce engagements (Mexico, Panama, Guatemala — rated Exceptional across all categories on signed PPQ), VA Lease Contract Administration (58 active VHA leases / 2.5M sq ft / 30%+ administrative time reduction), and the Syngenta GMO Corn expert-witness engagement (~$500M settlement) where Jelani synthesized U.S., Chinese, and EU regulatory frameworks.