Doc Timeline
Enterprise · SOC 2
Thousands of documents → one chronological timeline. AI extraction for legal discovery, claims, audits.
00 / soo-BOHD KAY-seev3.3.0 - Aug 2026
AI Advisor &
AI Systems Architect.
From possibility to decision.
From decision to production.
I turn AI ambiguity into possibilities, evidence-backed decisions, and systems organizations can actually operate. Research and critical inquiry, evidence over hype, architecture that survives production, program leadership that ships, and continuous improvement after launch.
products
18
shipped
installs
10k+
npm + pypi
programs
50+
AI delivered
fig.01 - deployed systemsclick a node ↓
01
See what you may be missing.
The most expensive AI mistakes are not bad decisions. They are the possibilities nobody examined. Six questions worth asking before the next budget cycle:
01
What could your customers stop waiting for?
02
What could your strongest people stop doing manually?
03
What decision would improve if evidence arrived sooner?
04
What capability is practical today that was not economical a year ago?
05
Where could one better system remove several downstream problems?
06
What should remain human?
Every possibility runs the same gauntlet: Research → Reframe → Prove & Decide.
The method ↓02
One method. Six disciplines.
Every engagement, from a $500 workflow assessment to an enterprise advisory relationship, runs on the same operating method. Each stage exists because it creates a specific kind of value.
01
Research
Understand reality before committing to it. Evidence about your workflows, constraints, customers, and market replaces assumption and vendor narrative.
02
Reframe
Find the opportunity behind the request. The stated problem is rarely the valuable one. Reframing turns a purchase order into a decision worth making.
03
Prove & Decide
Evidence, trade-offs, and a recommendation an executive can sign. Build, buy, configure, integrate, wait, or stop. The answer is allowed to be no.
04
Architect
System, data, human, and control design that survives production. What the AI does, what people keep, what happens when it is wrong, and how it is governed.
05
Mobilize
Ownership, dependencies, risks, and execution cadence. The decision becomes a program with named owners, sequenced work, and visible status.
06
Improve
Production evidence drives the next decision: scale, change, or stop. Systems that cannot be measured cannot be defended.
The same method, visible in shipped systems and published frameworks.
Selected work →03
Where should you start?
What could you be missing? What is actually worth pursuing? What decision should you make, and how would you make it real? Choose the path that matches your situation.
I want an AI advisor in my corner
AI Advisor for Business · $99/month
Ongoing human AI advisory. Weekly signal, monthly point of view, and human advice when a decision matters. See what is changing, what it could mean for you, and what deserves action.
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I want to find where AI could improve a workflow
AI Opportunity & Workflow Assessment · $500 fixed
One opportunity, one primary workflow. You receive an AI Automation Blueprint with tool selection, architecture, cost-benefit, and a clear buy/configure/build recommendation. $500 fixed, delivered in 5 business days.
Start My Assessment
I want an AI advisor in the room
Fractional AI Advisor · $1,250/month
Executive AI advisory for higher-stakes decisions. Strategy, architecture, vendor evaluation, build-vs-buy, and roadmap review with context that carries forward.
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CSM 2.0 · Based on the Original 2025 Framework
Cognitive Systems Management 2.0
Four governance domains. Six execution functions. Explicit decisions and evidence.
Version 2 adds deterministic governance contracts, evidence requirements and reassessment rules.
CSM-Enterprise
Establish the decision context before projects improvise it.
Question: Who has authority, who owns the outcome and risk, and what organizational boundaries apply?
•Policy Framework
•Risk Assessment
•Data Stewardship
•Strategic Mandate
Gives projects and technical teams clearer organizational direction, ownership and governance boundaries.
CSM-Project
Make scaling an explicit decision.
Question: What evidence should justify continuing, changing, scaling or stopping an AI initiative?
•Business Case Definition
•Controlled Testing
•Scale Decision Framework
•Playbook Documentation
Creates a deliberate decision boundary between experimentation and operational commitment.
CSM-Code
Keep AI-assisted development accountable.
Question: How should software engineering governance change when AI contributes to implementation?
•Development Standards
•Security Protocols
•Human Oversight
•Traceability Logging
Extends normal engineering review, security and accountability into AI-assisted development.
CSM-UX
Keep human judgment connected to the system.
Question: What do humans need to understand, supervise, challenge and appropriately use AI-supported outcomes?
•Impact Analysis
•Explainability Design
•Capability Development
•Adoption Measurement
Connects governance to real human use and creates feedback for improving the wider system.
Enterprise
Project
Code
UX
Feedback
The domains are governance lenses, not sequential project phases.
Enterprise decisions inform Project criteria.
Project criteria inform Code.
Implementation reality shapes UX.
Operational and user feedback flows back into Project and Enterprise governance.
04
Production systems
showing 02 / 18AI Compliance Law Guides
complete guides to the AI regulations that matter most - informed by the HAIEC compliance engine and Zenodo-published research.
- 2025ZenodoView paper◆ Latest
The Instruction Stack Audit Framework (ISAF)
A Technical Methodology for Tracing AI Accountability Across Nine Abstraction Layers
Nine-layer technical specification for documenting instruction propagation from hardware to outputs. 127-checkpoint audit protocol, cryptographic verification, abstraction-distance risk scoring.
AI GovernanceEU AI ActNIST AI RMFISO 42001Algorithmic Accountability - 2026SSRNView paper
Deterministic Bias Detection for NYC Local Law 144
Why Reproducibility Matters More Than Accuracy: A Technical Framework for Compliance-Grade AI Auditing
Presents a deterministic architecture for bias detection that prioritizes reproducibility over algorithmic sophistication. Demonstrates why probabilistic AI models cannot satisfy evidentiary requirements of regulatory compliance. Uses rule-based pattern matching, version-controlled lexicons, and cryptographic evidence generation. 35 pages.
NYC Local Law 144Bias DetectionRegulatory ComplianceDeterministic SystemsAlgorithmic AccountabilityEmployment DiscriminationReproducibilityCompliance Architecture - 2025ZenodoView paper
From Industrial Electrification to Artificial Intelligence
Institutional Lessons from Construction Governance for AI Risk Regulation
Analyzes institutional evolution of construction governance and applies structural lessons to AI risk regulation. Proposes a phased governance maturation model drawing from mechanization, electrification, occupational safety, professional licensing, and insurance enforcement.
AI Risk RegulationGovernance MaturationGeneral Purpose TechnologyInstitutional LessonsRegulatory Consolidation - 2025AI Governance PlaybookView paper
From AI Pilots to Regulatory Readiness
A Framework for Production-Grade, Compliant AI
Practical framework for transitioning from AI experimentation to production systems that survive regulatory scrutiny.
Framework PaperAI StrategyCompliance - 2025Design BootcampView paper
Why Enterprise AI Integration Strategies Fail
A Systematic Analysis
Common architectural and organizational failure modes in enterprise AI adoption, with patterns and counter-patterns.
AnalysisEnterprise Architecture - 2025AI Governance on MediumView paper
Cognitive System Management: A Framework for Enterprise AI Project Governance
Four-domain AI governance methodology - now evolved to CSM 2.0
Original publication of the Cognitive Systems Management (CSM) framework: a four-domain governance methodology comprising Enterprise, Project, Code and UX. Current version: CSM 2.0 (spec v2.0.0, 2026-08-10).
MethodologyAI GovernanceAI Strategy