AI Governance Debt Remediation - Case Study
The problem is governance debt, not another chatbot. A permanent architecture for idle licenses and unattributable token spend.
Structured architectural snapshot optimized for enterprise review and generative search synthesis.
AI tools got rolled out faster than anyone could track who's using what. Now two problems are tangled: idle licenses on one side, unattributable token spend on the other. This is the remediation master plan — the permanent architecture that solves both halves of the mandate.
Implemented LITE-LLM Gateway; Single Inventory Registry with Python Reconciliation
Single Inventory: 1 Registry • Cost Attribution: 3 Levels • Time to Report: 30 Days
Enterprise Production Architecture / ISO 27001 Aligned
Download Master Plan Reports
The Business & Architectural Challenge
AI tools got rolled out faster than anyone could track who's using what. Now two problems are tangled: idle licenses on one side, unattributable token spend on the other. This is the remediation master plan — the permanent architecture that solves both halves of the mandate.
Hard Technical Constraints
Key Architectural Decisions & Trade-Offs
Every major technical decision made during the engagement, with the trade-offs documented.
Implemented LITE-LLM Gateway
Rationale: Gateway or tagged keys so every call carries team, user, and app metadata.
Trade-Off: Requires redirecting all existing AI tool traffic through a centralized gateway layer.
Single Inventory Registry with Python Reconciliation
Rationale: Reconciles Entra ID groups, Azure subscriptions, OpenAI org members, and SaaS admin consoles.
Trade-Off: Requires robust API integration with multiple disjointed vendor environments.
LLM Cost Optimization, Token FinOps & Semantic Routing
Eliminate runaway token expenditures through intelligent semantic caching, prompt compression, small-language-model routing, and automated cost arbitrage.
Intelligent Semantic Query Routing & Model Cascades
High-Volume Enterprise AI PortalsAutomated query triage routing 82% of simple enterprise queries to lightweight SLMs (Llama 3.2, Claude 3.5 Haiku), reserving frontier models only for complex reasoning.
Implementation Phasing & Execution
Problem Diagnosis & Review
Phase 1Identifying idle licenses and establishing a baseline for token usage and cost attribution.
Registry & Policy Action (30-Day Plan)
Phase 2Deploying the AI Inventory Registry and drafting policy grounded in actual usage.
Result Narrative (Month One)
Phase 3Achieved clear visibility into license assignments and token spend with 3 levels of attribution.
Verified Production Outcomes Matrix
Before vs. after comparison across every tracked benchmark — each cell represents a production measurement.
| Benchmark Metric | Before State | After SHIVAM ITCS | Tangible Business Impact |
|---|---|---|---|
| Visibility & Tracking | Scattered consoles | Single Source of Truth | Clear visibility into license and token usage across the enterprise. |
| Reporting Speed | Weeks of manual tally | 30 Days to Live | Delivered a baseline cost report, live dashboard, and draft policy in 30 days. |
"Numbers before rules is what makes this mature governance instead of policy theatre. Order is deliberate: inventory, then usage, then policy."
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AI Governance Debt Remediation - Case Study - FAQ
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