Institutional control plane

A pre-spend governance engine for enterprise AI, not a monitoring tool.

Every AI workload passes through a five-stage economic control plane before execution. Decisions are logged, attributed, and auditable to corporate finance standards.

The platform intercepts consumption requests, simulates financial impact against budget state, and authorizes, constrains, blocks, or escalates the workload. Every outcome produces an attributed cost record and enterprise credit entry. Finance can reconcile it, Engineering can operate it, and executive stakeholders can understand the consequence.

Five-stage governance lifecycle Observe → Gate → Simulate → Execute → Audit

Five-layer economic control plane See the spend → control the spend → prove the value

Provider economics visual Normalized cost and quality comparison

Pricing as of June 2025. Bars show normalized cost index and quality score for executive comparison.

Capability brief Institutional outcomes without implementation disclosure

Capability	What It Does	Institutional Value

FOCUS alignment Provider-neutral finance data contract

FOCUS Field	Platform Field	Authoritative Source

All governance decisions emit FOCUS-aligned records. Bulk CSV export is available from the Ledger.

TBM taxonomy Technology cost → service ownership → business outcome

TBM Layer	Platform Capability	Business Outcome

FinOps operating posture Inform → Optimize → Operate

FinOps Stage	Platform Capability	Evidence

Governance API · headless endpoint Stable interface, protected implementation

Any system issuing AI inference requests can call the governance gate before execution.

POST /api/governance/evaluate
Content-Type: application/json

{
  "agent_id": "agent.fabrication.yield",
  "business_unit": "Fab Operations & Yield",
  "workload_type": "yield_prediction",
  "persona_tier": "developer",
  "requested_compute_units": 240000,
  "urgency_score": 0.8,
  "confidence_floor": 0.65,
  "budget_period": "2025-06",
  "cost_center": "FAB-2100"
}

Responses expose decisions and evidence references, never routing internals.

{
  "decision": "APPROVED",
  "governance_record_id": "GOV-2848",
  "allocated_provider": "Azure OpenAI",
  "allocated_model": "GPT-4o",
  "authorized_compute_units": 240000,
  "projected_cost": 2620,
  "risk_adjusted_roi": 4.2,
  "waste_prevented": 890,
  "confidence_score": 0.94,
  "denial_reason": null,
  "focus_record_uri": "/records/GOV-2848/focus"
}

Callable demonstration

curl -X POST https://agentic-finops-ledger.vercel.app/api/governance/evaluate -H "Content-Type: application/json" -d @intent.json

Designed for enterprise trust Control evidence, not certification claims

This public artifact demonstrates control design and auditable behavior. Formal certifications, tenant-specific residency commitments, and production SLAs require enterprise deployment scope.

Confidence transparency

The most valuable decision is sometimes no autonomous decision.

When evidence is insufficient, the platform returns ABSTAINED and routes the workload to a human reviewer rather than fabricating confidence. DENIED and ABSTAINED are governance outcomes, not failures.