What CPP Provides
Most AI engagements start with a pre-built solution looking for a problem to justify it. CPP starts with your operation — what it's spending, what it's exposing, what it's trying to build — and works from there. The work we scope for you is the work your specific situation requires. Not more. Not a retainer dressed up as strategy.
How the Work Gets Done
Every CPP engagement begins with the AI Opportunity Sprint — a structured, single-day diagnostic that produces deliverables you own when we're done. That means a model-spend baseline, a policy exposure review, and an agentic opportunity assessment calibrated to your operation. The Sprint is not a sales event. It's designed to give you a complete picture of where you are, whether or not we do anything else together.
The Sprint output drives every subsequent scoping decision. If there are gaps worth closing, we'll identify them with specificity. If there isn't a strong case for additional work, we'll tell you that too. No engagement should begin without the diagnostic clarity the Sprint provides.
Where Sprint findings point to specialized work — regulatory alignment, FinOps architecture, engineering workflow design — CPP draws on a network of domain experts matched to the problem at hand. These are not generalists deployed to fill a Statement of Work. They're practitioners with direct experience in the specific function, sector, or technical domain the engagement requires.
The engagement model keeps CPP responsible for coordination, quality, and outcome accountability. You're not managing a vendor relationship across three firms. You have one point of contact and one set of commitments.
Where ongoing AI operations require infrastructure — policy enforcement, attribution tracking, developer signal capture — CPP deploys purpose-built solutions and operates them as a managed service. You get the capability without the platform overhead.
This matters because most organizations that adopt AI tooling don't have the internal capacity to operate it well. CPP closes that gap. The technology does the work. CPP holds the operational accountability.
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.
No Pre-Built Stack
CPP does not have a product catalog. What we provide is determined by what your diagnostic reveals — not by what we already built and need to justify deploying. That means engagements look different from client to client, because the situations are different. A FinOps leader at a 400-person SaaS company has a different problem than a General Counsel at a financial services firm trying to demonstrate AI policy compliance to an auditor. Treating those as the same situation is how firms get sold solutions that don't fit.
The Sprint is the discipline that enforces this. It's structured specifically to prevent the engagement from being shaped by vendor preference rather than client need. The outputs belong to you. If you take the Sprint and go work with someone else, or decide you don't need outside help at all, that's a legitimate outcome. It means the diagnostic worked.
The managed service model exists because most organizations that need AI governance or attribution infrastructure don't have the internal capacity to operate it themselves. We run it. That's not an upsell — it's the honest answer to the implementation gap that kills most enterprise software deployments. We also have limits: we don't do staff augmentation, we don't run long-term embedded consulting engagements, and we don't take on work that requires us to pretend we know your business better than you do. If something falls outside what CPP does well, we'll say so.
Start With the Sprint
The AI Opportunity Sprint is how CPP engagements begin. It's a structured diagnostic with specific outputs — a model-spend baseline, a policy exposure review, an agentic opportunity assessment — that you own when we're done. If there's work worth doing beyond that, we'll tell you what it is and why. If there isn't, you'll know that too.
Schedule a Sprint Conversation