What CPP Builds and Runs
Each of these starts as a finding from an engagement, not a pitch. CPP builds the infrastructure, operates it as a managed service, and holds the operational accountability — so you get the capability without staffing the platform yourself.
For the CFO, VP Finance, and FinOps lead
Make AI spend visible, attributable, and shrinking.
Most mid-market organizations that have deployed AI tools don't have an accurate picture of what they're spending on them. Model API costs accumulate across teams. SaaS-embedded AI runs on flat licenses with no usage visibility. Shadow deployments sit outside any procurement process. The result is a cost center with no cost owner.
CPP's AI Cost Optimization engagement starts with the audit: a full inventory of what's running, where the spend is going, and which costs are attributable to which business functions. That attribution work is the prerequisite for everything that follows — you cannot manage what you cannot see.
From the audit, CPP builds a showback framework: a reporting structure that makes AI spend legible to finance and visible to the teams generating it. Where contract renegotiation or consolidation creates savings opportunities, we identify and quantify them. Where usage patterns suggest the wrong tools are running for the job, we say so. The goal is a cost structure you understand and can defend — not just a lower number on this month's invoice.
Who this is for
Organizations with AI spend across multiple teams or tools and no unified view of where the money is going. Companies that are renewing AI contracts without visibility into utilization. Finance leaders who need to answer board-level questions about AI ROI and currently cannot.
For the CISO, Chief Compliance Officer, and General Counsel
Enforce AI policy, prove it held, produce an audit trail a regulator can read.
Shadow AI is not a fringe problem. In most organizations that have not implemented formal AI governance, employees are using AI tools that were never approved, on data that was never cleared for external processing, in ways that create liability the organization doesn't know it has. The exposure is compliance, legal, and operational — often simultaneously.
CPP's AI Governance engagement builds a three-layer framework: policy (what is and isn't permitted), enforcement (the technical and procedural controls that give the policy teeth), and evidence (the audit trail that proves the controls held when it matters). This isn't a policy document delivered and forgotten. It's a governance posture that operates continuously, surfacing violations and producing the documentation regulators expect.
The audit trail CPP produces is immutable and structured for regulatory review. Whether the relevant standard is the EU AI Act, NIST AI RMF, or a sector-specific framework like HIPAA or SOC 2, the output is evidence a compliance officer can actually use — not a dashboard with a green checkmark.
Who this is for
Organizations with active or anticipated regulatory scrutiny of AI use. Legal and compliance teams that have issued AI policies but have no mechanism to verify adherence. Companies operating in regulated industries where AI use creates data-handling obligations that are currently unaddressed.
For the CTO, VP Engineering, and Engineering Manager
Separate real engineering velocity from the vibe-coding trap.
AI coding tools produce output volume. What they don't produce, on their own, is engineering judgment. The gap between the two is where technical debt accumulates — in accepted completions that bypass review, in AI-generated code that passes tests but degrades architecture, in the gradual substitution of throughput metrics for quality ones. Engineers feel more productive. The codebase gets harder to maintain.
CPP's AI Development OS engagement instruments the engineering workflow to make that gap visible. Using IDE-level signals we capture, we surface where AI assistance is generating value and where it's introducing drag — in code review patterns, in defect rates, in time-to-understand for future contributors. The output is a set of operating norms and tooling configurations that let engineering leaders make informed decisions about how AI fits into the development process, rather than discovering the consequences later.
This is early-access work. We're running it with a small number of engineering organizations to build the evidence base. If your team is using AI coding tools at scale and you're not confident you understand the quality tradeoffs, this is the engagement designed for that situation.
Who this is for
Engineering organizations that have adopted AI coding tools broadly and are starting to see patterns they can't fully explain — velocity that doesn't translate to throughput, review overhead that keeps climbing, or code quality signals that are moving in the wrong direction. CTOs and engineering managers who want a defensible picture of what AI assistance is actually doing to their codebase.
Not Sure Which One
None of these are sold off a shelf. The diagnostic tells you whether you need one of them, more than one, or none at all.