AI governance begins with applicability.
Sellhausen Consulting helps regulated organizations determine which AI requirements actually apply to their systems, decisions, vendors, and jurisdictions — then turns those requirements into controls, evidence, and review-ready governance files.
Most AI governance work produces policy. We produce applicability determinations, decision-control maps, and evidence packages that can survive examination, audit, procurement review, or oversight.
The problem
AI obligations do not apply evenly.
A federal agency, a state contractor, a $50B bank, a $5B credit union, and an AI vendor can all use AI — but their obligations are not the same.
Applicability depends on asset thresholds, regulator, jurisdiction, decision type, AI system type, vendor role, and whether the system affects consumers, citizens, borrowers, employees, applicants, or public services.
That is where governance has to begin.
The method
Applicability → Decision → Risk → Control → Evidence
We start by determining which sources apply. Then we map each AI system to the decision it influences, the people affected, the risks created, the controls required, and the evidence needed to prove the system is governed.
The output is not a policy binder. It is a review-ready evidence file.
- Applicability
- Which sources actually apply — by institution type, asset threshold, regulator, jurisdiction, AI system type, vendor relationship, and the business decision being influenced.
- Decision
- What decision the AI system influences, who is affected, and how the decision is reviewed.
- Risk
- The risks the system creates for the affected party and the obligations those risks trigger.
- Control
- The controls required to manage each risk, tied to a specific source and a specific decision.
- Evidence
- The artifacts a reviewer — examiner, auditor, IG, procurement officer, oversight committee — will accept as proof the system is governed.
Flagship assessment
AI Applicability & Decision-Control Assessment
For regulated organizations using AI in consequential decisions. We identify which AI requirements apply to your use cases, map those requirements to controls, and produce the evidence checklist needed for review, audit, examination, procurement, or oversight.
Deliverables
- Organization applicability profile
- AI use case inventory
- Source applicability matrix
- Decision-control map
- Required controls register
- Evidence checklist
- Governance gap register
- Monitoring queue
- 90-day remediation roadmap
Practice areas
Federal AI Governance & Acquisition Readiness
For agencies, contractors, and vendors that need to translate federal AI policy and procurement expectations into reviewable controls and evidence.
Focus areas
- — Agency AI governance and inventory
- — High-impact AI classification
- — AI acquisition and procurement requirements
- — Vendor documentation and oversight
- — Evidence files for IG, GAO, procurement, or program review
- — Federal contracting readiness
Financial Services AI Control Readiness
For banks, credit unions, fintech lenders, and financial-services vendors that need to understand AI, model risk, adverse action, vendor, and supervisory expectations at the use-case level.
Focus areas
- — Model risk and AI governance applicability
- — Sector-specific AI risk frameworks
- — Generative AI governance gaps
- — ECOA / Regulation B adverse action evidence
- — Third-party AI oversight
- — Monitoring, validation, and decision-control evidence
Vendor / Procured AI Evidence Review
For organizations buying, deploying, or selling AI systems in regulated environments.
Focus areas
- — Vendor documentation
- — Model and system cards
- — AI due diligence
- — Contractual safeguards
- — Data and IP rights
- — Testing and monitoring evidence
- — Downstream deployer obligations
Built from three vantage points
Former FDIC risk management examiner. Former Army Observer/Controller-Trainer. Former Fortune 50 AI and data product leader.
That combination matters because AI governance is not just regulation, not just technology, and not just organizational change. It is the discipline of making consequential systems reviewable, accountable, and defensible.
Federal contracting-ready
A Texas LLC, SAM-registered for federal AI work.
Sellhausen Consulting LLC is a service-disabled veteran-owned business based in Round Rock, TX. Registered in SAM.gov for federal contracting and active on the Texas Centralized Master Bidders List. Actively seeking teaming arrangements with prime contractors pursuing federal AI governance, acquisition, and procurement work.
- SAM.gov UEI
- RQJEPZ47MK68
- Federal designation
- Service-disabled veteran-owned business (SDVOSB certification in process, July 2026 target)
- NAICS codes
- 541611 · 541512 · 541519 · 541618 · 541690 · 541511
- Texas CMBL
- Active · 14229761185
- State
- Texas LLC · Round Rock, TX
What we help you answer
- Which AI requirements actually apply?
- Which sources are binding, supervisory, contractual, voluntary, withdrawn, or superseded?
- Which AI use cases influence consequential decisions?
- Who is affected — consumers, citizens, borrowers, employees, applicants, public services?
- What controls are required?
- What evidence do reviewers expect?
- What governance gaps need remediation?
- What source changes should be monitored?
Writing and field notes
Practical notes from the work.
Essays on AI governance, applicability, decision controls, financial-services AI, federal AI acquisition, and evidence-first governance.
Start with applicability
If your organization is using AI in regulated, consequential, or public-sector decisions, the first conversation is an applicability conversation.