Applied AI Research
Reasoning Requires Situational Awareness,
Not Just Better Models
Enterprise AI fails at the decision layer because agents lack real-time organizational context. CTRS (Control Tower Reasoning System) provides the architectural blueprint.
I build frameworks that turn AI from prediction engines into decision-grade reasoning systems. They're grounded in Enterprise Digital Twins that provide situational awareness, enforce governance, and close the $50B gap between pilots and production.
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Core Frameworks
Decision Velocity
Build frameworks that turn AI pilots into production systems with measurable ROI
Version Drift Prevention
Engineer trust architectures that survive model updates, vendor shifts, and regulatory changes
Agent Orchestration
Connect enterprise data to AI agents through MCP patterns
FEATURED IN
Research cited by leading organizations
Frameworks and insights referenced by Fortune 500 companies, research institutions, and AI platforms
Academic Citations: Articles also cited in research papers on arXiv, SSRN, IEEE publications, and university theses
THE APPROACH
Legal Mind, Technical Hand
Law degree. Two decades of building enterprise systems. The last several years focused specifically on AI strategy and applied research. This dual perspective reveals patterns most technologists miss in enterprise AI architecture.
Legal training taught me how to think about systems of rules, compliance boundaries, accountability chains, and governance structures. Most AI researchers don’t come from this background. They optimize for model performance without understanding the regulatory constraints that determine whether AI actually ships to production.
Twenty years of building enterprise solutions across showed me where AI initiatives actually fail: not at the model layer, but at the decision layer, not because of accuracy, but because of the architectural gap between prediction and action.
The $30-40 Billion Blind Spot
Enterprises optimize for the wrong metrics. Then they wonder why AI doesn't show up in the P&L.
You've deployed models. You've hired ML engineers. You've celebrated accuracy improvements. But AI initiatives stall because you're measuring model performance instead of decision outcomes.
What Enterprises Measure:
- Model accuracy (AUC, F1, precision/recall)
- Technical milestones (models deployed, API calls)
- Vanity metrics (tokens consumed, copilot adoption)
- Infrastructure spend (GPU hours, compute costs)
- Data volume (terabytes ingested, features engineered)
What Actually Drives ROI:
- Decision Velocity – Time from insight to action
- Governance Coverage – % of decisions with clear ownership
- Override Rate – Human rejection signals broken workflows
- Audit Readiness – Explainable decision lineage
- ROI per Decision Stream – Value created, not models deployed
The gap between prediction and action is where billions in AI investment disappear.
This is the gap CTRS closes, and the architecture that sustains it.SIGNATURE FRAMEWORK
The CTRS Architecture:
Control Tower Reasoning System
Reasoning requires situational awareness grounded in an Enterprise Digital Twin. CTRS provides the architectural blueprint for AI systems that understand context, ensure compliance, and execute decisions (not just generate predictions).
Decision Intelligence
The North Star Metric
Your AI investments are measured by model accuracy. They should be measured by Decision Velocity: the speed at which intelligence becomes action.
- Define: Decision Rights & Ownership
- Design: Decision Streams & Workflows
- Deploy: Governance as Infrastructure
- Defend: Audit Trails & Compliance
Enterprise Digital Twin
Situational Awareness Layer
AI agents need real-time knowledge of organizational state: policies, roles, constraints, and context. The Digital Twin provides this foundation for reasoning.
- Live policy versioning (not static RAG)
- Role-based access & context injection
- Compliance state as first-class data
- Decision history & audit lineage
Trust Layer Architecture
Version Drift Prevention
The hidden compliance time bomb: AI retrieves "correct" but outdated policies. Trust Layer prevents it through dual-index governance.
- Temporal knowledge graphs
- ContentOps as discipline
- Deprecation detection & alerts
- Regulatory change propagation
Agent Orchestration
MCP Integration Fabric
Autonomous agents amplify risks without standardized context management. Model Context Protocol (MCP) provides the integration fabric.
- Secure, standardized data access
- Agent coordination & handoffs
- Governance enforcement at runtime
- Scalable multi-agent workflows
IMPLEMENTATION BLUEPRINTS
Three Frameworks That Fix Enterprise AI
Each framework diagnoses root causes (not symptoms) and provides architectural blueprints for production-grade AI systems in regulated environments.
Decision Velocity
The New North Star Metric
Why AI investments don't show up in the P&L, and the 4D framework (Define → Design → Deploy → Defend) that turns predictions into profit.
- Decision Intelligence as discipline
- Chief Decision Officer mandate
- Decision Review Board governance
- Velocity metrics & dashboards
- 90-day implementation roadmap
Version Drift
The Hidden Compliance Time Bomb
How AI retrieves "correct" but outdated policies, and the Trust Layer architecture that prevents it. Case study: Air Canada's lawsuit.
- Why RAG isn't enough
- Dual-index governance model
- ContentOps as discipline
- Temporal knowledge graphs
- Banking & Healthcare implementations
Model Context Protocol
Integration Fabric for AI Agents
Why autonomous agents need standardized context management, and how MCP enables secure, scalable integration in regulated environments.
- The agent amplification problem
- Security & governance implications
- Enterprise implementation patterns
- Multi-agent orchestration
- Future of agentic workflows
FEATURED RESEARCH
Latest Insights
Recent analysis on decision-centric AI architecture, reasoning systems, and enterprise implementation patterns
★ CORNERSTONE FRAMEWORK
The Architecture Gap: Why Enterprise AI Governance Fails Before It Starts
Most enterprise AI governance programs produce policies, not proof. When regulators examine your AI systems, they ask for decision lineage, audit trails, and version control. They find committees and principles.…
Read full framework →Enterprise AI: An Analysis of Compound Architectures and Multi-Agent Systems
Enterprises are moving from single model apps to coordinated systems that plan act and learn across real workflows. This article explains how to design and run compound AI and multi…
Read article →The Enterprise AI Problem Nobody Budgeted For: Version Drift
Beyond AI hallucinations, a more dangerous enterprise risk exists: Version Drift. This quiet failure happens when AI systems, though not creating false information, pull and cite outdated policies that have…
Read article →Decision Velocity: The New Metric for Enterprise AI Success
The persistent failure of enterprise AI isn't a technical problem; it's a strategic one. While Enterprises refine predictive models, they often fail to act on the insights they generate, leaving…
Read article →WHO THIS IS FOR
Three Entry Points
Curated entry points based on your strategic priorities and decision-making responsibilities
C-SUITE & VPs
CIOs & Chief Risk Officers
Your Problem:
AI pilots stall. Board demands ROI you can't demonstrate. Governance creates friction, not safety.
What You Need:
Decision Velocity framework: measure speed-to-action, not model accuracy. Governance as infrastructure, not bottleneck.
ENTERPRISE ARCHITECTS
AI & Data Architects
Your Problem:
Models accurate but unused. Version control chaos. No clear PoC-to-scale path. High override rates.
What You Need:
Trust Layer architecture, MCP orchestration patterns, and production-grade compound AI system designs.
REGULATED INDUSTRIES
Banking, Healthcare, Insurance
Your Problem:
Compliance blocks deployment. No audit trails. Can't explain decisions to regulators. Risk of penalties.
What You Need:
Compliance-first architecture, explainability frameworks, and audit-ready decision lineage systems.
RESEARCH PORTFOLIO
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