Build · Deploy · Assure
AI deployment assurance for regulated environments.
Production AI in regulated industries demands more than accuracy. It demands trust, traceability, and governance. We build the operating model that delivers all three.
Operating method
- 01
Build
Scope and construct the workflow
- 02
Deploy
Install it in real operations
- 03
Assure
Prove it keeps working
AI in regulated environments cannot afford to fail quietly.
Enterprises and government organizations face a unique challenge: AI that works in a lab does not automatically work in production. Regulated environments require governance, monitoring, accountability, and audit trails — not just model performance. Without an operating model for AI assurance, every deployment is a risk.
- AI pilots succeed but never reach production
- Governance frameworks exist on paper but not in operations
- No clear accountability for AI decisions in production
- Compliance teams cannot verify what AI systems are doing
- Incident response for AI failures does not exist
Who This Is For
Deployment assurance is built for organizations where AI failure is not an option.
Enterprise AI/ML Teams
Organizations with active AI initiatives that need governance and operational rigor to move from pilot to production.
Government Contractors
Defense and federal contractors navigating AI deployment under DoD AI Ethical Principles, NIST AI RMF, and emerging mandates.
Regulated Industries
Healthcare, finance, and defense organizations where AI deployment carries compliance, safety, and liability requirements.
Organizations with Active AI Pilots
Teams that have proven AI works in the lab and now need the operating model to deploy it into production environments.
Risk & Compliance Leaders
Executives responsible for ensuring AI systems meet regulatory, ethical, and operational standards before and after deployment.
The Problems We Solve
These are the operational gaps that prevent AI from reaching production — and the risks that persist when it does.
No governance framework for AI in production
AI systems operate without defined policies, review gates, or decision rights — creating unmanaged risk.
No monitoring or observability for deployed models
Models degrade, drift, or fail silently with no system to detect, alert, or respond.
No accountability for AI decisions
No clear ownership of AI outputs, no RACI matrix, no escalation path when something goes wrong.
Compliance gaps across AI systems
Regulatory requirements exist but are not mapped to actual AI deployments — leaving audit exposure.
No incident response for AI failures
When an AI system produces harmful, biased, or incorrect outputs, there is no playbook for response.
Engagement Models
Three structured engagement models — each designed to meet organizations at their current stage of AI deployment maturity.
Production AI Readiness Assessment
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A rigorous assessment of your organization's readiness to deploy AI into production environments. Covers governance, data, infrastructure, risk, and workforce readiness.
- 01AI use case inventory
- 02Risk tiering
- 03Ownership map
- 04Governance gap assessment
- 05Evaluation and readiness criteria
- 06Evidence requirements
- 0730/60/90-day roadmap
- 08Executive briefing
AI Assurance Operating Model Sprint
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A structured engagement to design and implement an AI assurance operating model — the governance, monitoring, and accountability framework required for production AI in regulated environments.
- 01Governance workflow
- 02Intake process
- 03Review gates
- 04Risk classification model
- 05Evaluation criteria
- 06Monitoring cadence
- 07Decision rights
- 08Escalation model
- 09Documentation templates
Fractional AI Deployment Partner
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Embedded, ongoing leadership for AI deployment in regulated environments. Acts as your fractional head of AI deployment — bridging strategy, engineering, governance, and operations.
- 01Monthly executive advisory
- 02Governance council support
- 03Use case review
- 04Vendor and model review
- 05AI roadmap guidance
- 06Operating model maturity tracking
The Build, Deploy, Assure Method
Every engagement follows a structured methodology designed for the rigor required in regulated environments.
01
Build
Design and construct the AI workflow
02
Deploy
Ship it into real operations
03
Assure
Verify it works and keeps working
The method adapts to your regulatory landscape, organizational complexity, and deployment maturity. Whether you are deploying your first production model or governing a portfolio of AI systems, the discipline remains the same.
Sample Deliverables
Every engagement produces tangible, operational artifacts — not slide decks.
Governance Frameworks
- AI governance policy documents
- Decision rights and RACI matrices
- Review gate definitions
Risk Registers
- AI use case risk tiering
- Risk classification models
- Mitigation tracking dashboards
Monitoring Dashboards
- Model performance tracking
- Drift detection alerts
- Operational health metrics
Compliance Mappings
- NIST AI RMF alignment
- EU AI Act readiness
- Industry-specific regulatory mapping
Operating Procedures
- Incident response playbooks
- Escalation protocols
- Documentation templates
Frank Sellhausen has led AI and data products at Fortune 50 scale, worked as a regulator, and still builds and ships. About
Frequently Asked Questions
Next step
Build the trust infrastructure your AI needs.
Start with a conversation about your AI deployment landscape, regulatory requirements, and organizational readiness. We will map the path from pilot to production-grade assurance.
Schedule a ConsultationStart a Conversation
Tell us about your AI deployment landscape and we will identify the right starting point.