Vertex Operations Group is an 85-employee professional services and operations company in the SaaS-adjacent space. Approximately $12M in annual revenue [measured]. Like many companies their size, AI adoption happened fast and without governance — tools purchased by individual teams, premium models used for commodity tasks, and no framework to evaluate whether any of it was working. Their CFO's exact question: "Are we in control?"
Vertex had adopted AI tools the way most growing companies do — reactively. A team lead saw a demo. Someone expensed a subscription. IT approved a request without a use-case review. The result: 11 active AI subscriptions, 5 of which had fewer than 3 active users [measured], 3 pairs of duplicate tools [measured], premium models being used for tasks that cheaper models handle identically, and — critically — 2 tools with customer-adjacent data flowing through consumer-grade AI APIs with no data processing agreements in place. [measured]
The AI Control Score measures governance, attribution, cost efficiency, risk exposure, and tool utilization. A score below 40 indicates critical exposure. Vertex moved from critical to controlled in 60 days.
Vertex's Proof Audit followed the Coriven Method across all four phases. Total engagement cost: $18,000 [measured]. Total client time invested: ~24 hours [estimated].
Inventoried all 11 AI subscriptions through credit card statements, IT asset records, and department manager interviews. Mapped actual usage via tool admin dashboards and API call logs. Identified data flows for each tool — what data enters, where it goes, and under what terms. Scored each tool on utilization, cost efficiency, ROI attribution, risk exposure, and strategic alignment. Applied model-tiering analysis to assess whether premium models were justified.
Built the AI governance framework — a lightweight but real approval process with ownership fields, use-case documentation, and quarterly review triggers. Cancelled 5 tools identified as unused or duplicative. Configured model routing rules for the remaining high-value tools — premium models only for defined complex task types. Replaced the 2 non-compliant tools with compliant alternatives. Established ROI tracking for the 4 remaining strategic tools with defined success metrics and measurement cadence.
Trained department heads on the AI governance framework in a 90-minute session — what requires approval, how to document a use case, how to propose a new tool. Trained IT on the vendor review checklist and BAA requirement for any tool processing non-public data. Trained 4 tool owners on their new ROI measurement responsibilities. CFO briefed directly on the control score methodology and measurement approach. Framework adopted immediately — first approval request came in the day training closed.
Measured AI spend at 30 and 60 days post-implementation. Tracked via centralized spend dashboard built from company card data. Monthly AI spend decreased from $8,200 to $4,900 — $3,300/month, $39,600/year reduction. AI Control Score reassessed at 60 days: 28 → 82 out of 100. Risk exposure resolved — no flagged tools remain in non-compliant configuration. 4 tools now have documented ROI owners and measurement baselines. 0 new tools subscribed without governance approval.
Each finding scored on a 5-point weighted model: cost impact, risk exposure, speed to resolve, governance complexity, and strategic importance.
| Finding | Score | State at Audit | State at 60 Days |
|---|---|---|---|
|
2 Tools — HIPAA-Adjacent Data, No BAA
Compliance Risk · Data Governance
|
5.00 Do First | Customer-adjacent data flowing through consumer AI APIs — no BAA, no data processing agreement [measured] | Tools replaced with compliant alternatives — BAA in place for all remaining AI vendors [measured] |
|
3 Duplicate Tool Pairs — $1,400/mo Waste
Cost Efficiency · Tool Rationalization
|
4.60 Do First | 2 summarization tools, 2 content generation platforms — paying for both, no consolidation [measured] | Consolidated — 3 duplicate subscriptions cancelled, single tool per function [measured] |
|
5 Tools — Fewer Than 3 Active Users
Utilization · Cost Waste
|
4.30 Do First | 5 subscriptions with <3 active users — paying for access nobody is using [measured] | 5 tools cancelled — spend eliminated, affected users migrated to retained tools [measured] |
|
Premium Models on Commodity Tasks
Cost Efficiency · Model Tiering
|
3.80 Do Next | GPT-4o used for email drafting, summarization, data formatting — commodity tasks at premium pricing [measured] | Model routing rules configured — GPT-4o-mini and Claude Haiku for commodity tasks [measured] |
|
Zero ROI Attribution Across All Tools
Governance · Business Case
|
3.50 Do Next | Not one AI tool tied to a measurable outcome — no owner, no metric, no baseline [measured] | 4 strategic tools now have documented ROI owners and measurement baselines [measured] |
|
No Approval Process for New AI Tools
Governance · Process Control
|
3.20 Do Next | Any employee could subscribe to any AI tool on a company card — no review, no awareness [measured] | Governance framework live — approval required, use-case documented, 90-day review scheduled [measured] |
Monthly AI spend reduced from $8,200 to $4,900 immediately at implementation [measured]. No new AI tools subscribed without governance approval since framework launch [measured]. CFO has a clear answer to "are we in control?" — with a score, a framework, and a quarterly review schedule.
The 4 retained high-value tools now have measurement baselines. Phase 2 turns those baselines into ROI proof — and uses the data to negotiate better pricing from AI vendors who know Vertex is now a managed, consolidating buyer.
We inventory your AI stack, score your control posture, eliminate waste, resolve risk, and build the governance framework so the answer is always yes. Starting at $7,500 for a 4-week sprint.
Start the Conversation →Disclaimer: This case study is based on a simulated engagement using the Coriven Method. "Vertex Operations Group" is a representative company profile. All findings reflect the methodology Coriven applies to real engagements. Numbers tagged [measured] reflect verified data within the simulation. Numbers tagged [estimated] are calculated from baseline data and implementation modeling. Actual results vary.
All numbers carry measurement tags — [measured] or [estimated] — because we believe in transparency. If we can't measure it, we say so.