AI Governance Advisory, Assessment & Conformance (AGCP)
A structured lifecycle for governing AI decision systems—from design through deployment
The Problem
Most organizations deploying AI systems today are operating without a clear way to control how decisions are executed in production:
- AI-driven decisions execute without deterministic policy validation
- Governance logic is implicit, fragmented, or embedded in code
- Decisions are not fully auditable or reproducible
- Cross-domain risks (identity, infrastructure, supply chain) are not synchronized
- Regulatory pressure (NIST, ISO, EU AI Act) is increasing
In practice, this means AI systems are often making or influencing decisions without enforceable controls, creating risk exposure at the point of execution.
What We Do
We help organizations put deterministic governance around AI-driven decisions—so actions are controlled, auditable, and policy-bound at the point of execution:
- Policy-bound before execution
- Auditable and traceable
- Aligned across domains and risk models
- Safe to deploy and scale
Learn More About the AGCP Framework
For a deeper technical and architectural overview of how this is implemented:
Engagement Lifecycle
All engagements follow a structured lifecycle.
Scope and depth vary by system and organizational stage.
Phase 1 — Assessment (Entry Point)
Understand your current system and identify governance gaps
AI Governance Assessment
- Map AI systems and decision flows
- Identify governance gaps and execution risks
- Evaluate regulatory alignment
- Define required control points
Output:
- Governance gap analysis
- Risk classification
- Initial control-plane design
- 90-day roadmap
Duration: 2–3 weeks
Fee: $2,500 – $5,000
Phase 2 — Conformance / Alignment
Validate that your system behaves correctly under governance constraints
AI System Conformance & Alignment
- Validate system architecture against governance requirements
- Ensure decisions are policy-bound and auditable
- Align system behavior across risk, identity, and execution layers
- Identify cross-domain inconsistencies
Output:
- Conformance validation report
- Required architectural adjustments
- Governance alignment recommendations
Scope and pricing are defined based on system complexity.
Phase 3 — Implementation
Embed governance controls into real system workflows
Governance Implementation Support
- Design and integrate governance control points
- Define execution checkpoints and enforcement layers
- Embed policy validation into system workflows
- Support rollout across environments
Scoped based on assessment findings.
Phase 4 — Advisory
Support ongoing governance, scaling, and regulatory alignment
Ongoing Governance Advisory
- Strategic guidance and architecture evolution
- Governance maturity progression
- Risk posture alignment
- Regulatory readiness support
Structured based on organizational needs.
Applicability Across Organizations
This lifecycle applies across all stages of AI system development.
All engagements begin with the Assessment phase, with progression based on system needs and organizational stage.
The depth of each phase varies based on system complexity and organizational needs.
Founders & Early-Stage Teams
- Typically: Assessment → optional Conformance
- Focus: building systems that are governable by design
- May validate prior to launch, funding, or enterprise sales
Product Teams
- Typically: Assessment → Conformance → selective Implementation
- Focus: aligning AI features with governance requirements
Outcomes
After engagement, organizations can:
- Prevent uncontrolled AI-driven actions
- Enforce policy before execution
- Achieve auditability and traceability
- Align with NIST, ISO, and EU AI Act expectations
- Operate AI systems with confidence and control
Prefer to review the framework first?
Get Started
Most engagements begin with the AI Governance Assessment.
Schedule a Call
A focused 20-minute conversation to:
- understand your current AI systems
- identify governance gaps
- determine whether the Assessment engagement is a fit