Your board approved the AI initiative.
Find out if your data can support it before your next model risk exam.
Since July 2024, third-party AI arrangements don't remove institutional accountability. Every vendor tool you've deployed is your governance responsibility to validate, document, and defend.
The Platform Readiness Assessment is a fixed-price, scoped engagement that tells you exactly what your data foundation can support, what it can't, and what closing each gap will cost, before the examiner asks.
Three deliverables. Fixed price. Scope and timeline confirmed on your first call. Everything belongs to you regardless of what you decide next.
Current-state assessment across 10 data domains, including model governance and SR 11-7 posture
Architectural roadmap with Decision Matrix built for regulatory defensibility
Cost model with scenario projections, including regulatory remediation estimates
We'll review your submission and reach out within one business day to schedule a call.
The problem isn't your AI vendor. It's the governance gap underneath them that your institution owns.
The problem isn't your AI vendor. It's the governance gap underneath them that your institution owns.
Your AI vendor evaluated the use case. Your model risk team evaluated the tool. Nobody evaluated the data foundation the models will run on, or whether the governance trail can satisfy an examiner.
That gap is where most financial institution AI initiatives break down. Not in the demo. During the exam, when the regulator asks for adverse action documentation your current stack can't produce, or when the third-party accountability question surfaces tools you deployed without a validation plan.
Since July 2024, the OCC, Fed, and FDIC have been explicit: third-party AI arrangements don't remove institutional accountability. Every vendor model you run is your governance problem. If you can't document the validation, you own the finding.
The Wells Fargo story isn't about a bad model. It's about data lineage failures and governance gaps that were in place long before the discrimination became visible. That pattern isn't unique to Wells Fargo.
The Platform Readiness Assessment closes that gap before the examiner surfaces it for you.
Three Deliverables. Every Answer Your Regulators, Your CRO, and Your Board Need Before You Commit.
Here's exactly what you walk away with.
Current-State Assessment
A structured review of your data foundation across 10 discovery domains: architecture, governance, data quality, pipeline health, metric definitions, model inventory, compliance posture, and more. Mapped against what your AI initiative requires and what SR 11-7, CFPB Circular 2022-03, and the July 2024 third-party guidance actually require you to document.
Which means when the examiner asks about model governance gaps, adverse action documentation, or third-party AI validation, you have documented evidence of where you stand, not an explanation you're constructing in the moment.
Architectural Roadmap and Decision Matrix
Specific recommendations for what to build, fix, or retire, prioritized by regulatory impact and sequenced by dependencies. The Decision Matrix gives your CIO, CRO, and board a side-by-side view of options, tradeoffs, and implementation order, with the reasoning documented so every decision is defensible.
Which means your CRO walks into the next board meeting with a credible, exam-ready remediation roadmap, not a gap list and a timeline to be determined.
Cost Model and Scenario Projections
A detailed cost model across build scenarios: what it costs to close each governance and foundation gap, what it costs to carry the exposure, and what the AI initiative realistically requires at each stage. Regulatory remediation costs, professional services, and infrastructure costs all modeled explicitly across scenarios.
Which means your CFO frames the budget as documented risk mitigation with a clear payback story, not an AI technology investment, and your next exam cycle has a defensible answer already prepared.
Value Stack
Here's exactly what you walk away with.
| What you get | Standalone consulting cost |
|---|---|
| Current-State Assessment across 10 domains (model risk + SR 11-7 posture) | $18,000 |
| Architectural Roadmap and Decision Matrix | $15,000 |
| Cost Model and Scenario Projections | $12,000 |
| Executive Leadership Presentation | $5,000 |
| Total value | $50,000 |
The second payment is due only upon delivery of all three documents. If the deliverables aren't complete and clear by the confirmed end date, we keep working until they are, at no additional charge. Every deliverable belongs to you regardless of what you decide next.
How It Works
How the Assessment Works
The Platform Readiness Assessment runs in three focused phases, moving from initial discovery through in-depth analysis to final cost modeling and delivery so leaders can act with confidence.
Phase 1: Discovery and Assessment
Structured interviews with your data, engineering, model risk, and compliance teams. Architecture, pipeline, model inventory, governance documentation, and metric definition audit. You come out of this phase with a complete current-state picture across all 10 domains, mapped against SR 11-7 and applicable CFPB requirements.
Phase 2: Analysis and Architecture
Gap identification between your current foundation and what your AI initiative requires, including model governance gaps that create regulatory exposure. Architectural recommendations developed. Decision Matrix built. Implementation roadmap drafted with sequencing and dependencies mapped. Draft recommendations are ready for your internal review before Phase 3 begins.
Phase 3: Cost Modeling and Delivery
Cost model built across all scenarios, including regulatory remediation costs and the cost-of-inaction math your CFO and CRO need. All three deliverables delivered in a final leadership presentation. You leave the engagement with everything your CIO, CRO, and General Counsel need to make the next decision with confidence.
$30,000 to Know Or an Exam Finding to Find Out the Hard Way
A regulatory finding on model risk costs more than fixing the governance problem that caused it. Add the remediation cost, the examiner scrutiny that follows for the next two cycles, and the legal exposure on any adverse action decision that can't produce a compliant specific reason, and the math changes dramatically.
The same scope of work billed hourly at market rates runs $45,000 to $50,000. The Platform Readiness Assessment delivers it for $30,000, fixed, with a guarantee that all three deliverables are complete before the second payment is due.
That's not a consulting fee. That's the price of walking into your next exam cycle with a documented answer.
TESTIMONIALS
What Clients Say About Working With Macer
We've had a long term engagement with Reeves and Macer Consulting for the last few years for data engineering services. During this time, Reeves has consistently exceeded all expectations. His skills have been essential for the successful deployment of our current data platform, where he contributed not just to the selection of every tool in use but the implementation as well. He doesn't hesitate to chip in wherever helpful, on many occasions in areas well outside his responsibilities, and he brings an enthusiastic, friendly personality to every encounter. As of today nearly the entire business has been directly, positively impacted by his work. Reeves is, in my experience, a rare example of a consultant that genuinely works not just to the letter of the contract but to the improvement of the people and environment he finds himself in as well. I wouldn't hesitate to recommend him and Macer Consulting to anyone in search of data services. The skills and people are absolutely top notch.
Reeves and Macer brought the architecture clarity we needed to modernize our data platform. They led the Snowflake implementation, introduced Dagster for orchestration, and built out the CI/CD pipeline from the ground up. What stood out was the combination of hands-on execution and architectural thinking. We didn't just get the work done, we got a foundation we could actually build on. I'd recommend Macer to any analytics leader trying to move fast without cutting corners on the foundation.
The highest importance in a firm and resources they provide is the experience of design and architecture. It's easy to find an individual that can work with data or develop any area of technology. However, it's even more crucial to land resources that know the most successful structures, stable, effective and most of all, add value to a business while not costing a small fortune. Macer consists of that quality at the highest of levels.
Frequently Asked Questions
What is the Platform Readiness Assessment?
A fixed-price, scoped engagement that evaluates your data foundation against what your AI initiative actually requires, including your current SR 11-7 governance posture, CFPB adverse action documentation capability, and third-party AI accountability gaps. Three deliverables: a current-state assessment, an architectural roadmap with Decision Matrix, and a cost model with scenario projections. Scope and timeline are confirmed on the first call. Everything belongs to you regardless of what you decide next.
Who is this for?
Data and technology leaders at financial institutions who have an AI initiative on the table, a regulatory exam approaching, or vendor AI tools deployed without a full governance review. It is most useful when a decision needs to be made and the cost of a wrong answer is a regulatory finding.
How much time does this require from our team?
Structured interviews during the discovery phase typically require 2-3 hours total across key stakeholders: your data lead, engineering lead, model risk manager, and a compliance representative. After that, the work is on our side until delivery.
What happens after the assessment?
A final presentation to your leadership team covers all three deliverables. If the findings point to an implementation engagement and you want to discuss scope, the roadmap the assessment produces is the foundation for that SOW. There is no obligation to continue.
What is the guarantee?
The second payment ($15,000) is due only upon delivery of all three documents. If the deliverables aren't complete and clear by the confirmed end date, we keep working until they are, at no additional charge. This guarantee has never been invoked in 25 years of similar engagements.
Why $30,000?
Scope is defined before the engagement starts. The same work billed hourly at market consulting rates runs $45,000 to $50,000. At $30,000 fixed, the second payment isn't due until all three deliverables are in your hands. Most institutions that complete the assessment proceed directly to implementation with the roadmap already written, and with documentation they can use in the next exam cycle.
Does this cover SR 11-7 and CFPB adverse action requirements specifically?
Yes. The current-state assessment maps your model governance posture against SR 11-7 requirements, evaluates your ability to produce specific, accurate adverse action reasons under CFPB Circular 2022-03, and assesses your third-party AI arrangements against the July 2024 interagency guidance. The assessment tells you exactly where your exposure is, not a general governance gap analysis.
What if we already have vendor AI tools deployed?
That is exactly when this is most valuable. Under the July 2024 OCC/Fed/FDIC guidance, third-party arrangements don't remove institutional accountability. The assessment maps your current vendor tools against your governance obligations and tells you specifically what documentation and validation you need to close the gap before the exam asks.
Is this platform-agnostic?
Yes. The assessment evaluates your data foundation and governance posture, not your tooling choices. If you've already selected a platform or vendor, the assessment tells you whether your foundation is ready to support it. If you haven't, the Decision Matrix helps inform that decision.
Sound familiar?
If this feels familiar, the issue may be deeper than the platform itself
A lot of financial institutions made serious investments in cloud data infrastructure expecting faster reporting, better visibility, and stronger decision-making
More compute
More headcount
More manual effort
More internal friction
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