Start with the runnable reference system: use representative data, trace a decision to its supporting evidence, inspect failure behavior, and review the boundary between proposal and action. The remaining sections separate privately documented enterprise outcomes, sanitized architecture and implementation evidence, and publicly validated robotics.
START HERE: 5-MINUTE TECHNICAL REVIEW
Run it. Trace it. Challenge it.
Four direct paths through the flagship system, followed by three supporting evidence cases.
01PUBLIC-SAFE CODE + REPRODUCIBLE EVALUATION
ChangeFront Reference Edition
FLAGSHIP AI GOVERNANCE SYSTEM
A working local app whose MCP interface lets an AI client draft from approved evidence while a person keeps final authority.
ChangeFront stores trusted sources, tracks when they change, requires recommendations to show their supporting evidence, and sends the work to a human for approval or rejection. The AI can help create the work, but it cannot approve a decision or change an external system.
ACTION MODELHuman approval required; no automatic external writes
CLAIM BOUNDARYIndependent portfolio implementation; not employer software or hosted production
VERIFIED ENTERPRISE OUTCOMES
Results, evidence type, and claim boundary.
These outcomes were checked against private source records. The records stay private; only the minimum public-safe result and evidence level appear here.
01
Controlled AI pilot
Implemented a working pilot for a restricted user group, then limited access when validation exposed an external-search path outside the intended boundary.
Verification: private implementation and Teams records support the environment, participant access, validation finding, and gate decision; third-party records verify specialized control and capacity approvals. Boundary: exact configuration, user identities, production adoption, and service levels are withheld or not claimed.
02
Regulated AI decision
Recommended direction selected; project plan received all required cross-functional approvals.
Verification: leadership confirmation and a completed agreement notice. Boundary: production adoption and business impact are not claimed.
03
Enterprise ChatGPT migration
Supported the move of an early ChatGPT pilot used by a business team into the corporate workspace, followed by documented stakeholder acceptance and a completed service survey.
Verification: assigned migration ticket, stakeholder response, meeting objective, and survey notification. Boundary: team size, retained-history count, adoption, and business value are withheld or not claimed.
04
Enterprise AI deployment and operations
Deployed a workspace-wide internal GPT, published onboarding and usage guidance, and applied safeguards across a 100+ user administrative scope.
Verification: workspace deployment message plus private admin, leadership, guidance, onboarding, and support records. Boundary: active-user count, costs, realized savings, quality change, and adoption change are withheld or not claimed.
05
Scoped AI leadership
Selected for program and technical responsibilities in an AI enablement cohort and as the internal point person for governance discovery.
Verification: manager assignment, internal introduction, and governance point-person assignment. Boundary: these were scoped responsibilities; a formal AI Lead title, direct reports, budget authority, and enterprise-wide decision rights are not claimed.
I turn ambiguous security and business constraints into explicit boundaries, owners, and acceptance gates.
The private work reached a controlled pilot and a signed cross-functional baseline. Validation then exposed an external-search path outside the intended boundary, so I restricted access instead of treating configuration as proof of safety. The public artifact reconstructs the method while excluding employer-specific and controlled details.
Implementation, user-path validation, control mapping, and an evidence-based release gate.
Does not claim
A public copy of the employer design, a completed scaled rollout, or measured production impact.
03PRIVATE IMPLEMENTATIONS + SANITIZED EVIDENCE
Enterprise AI deployment and operations
Five private implementation patterns show how I move from AI capability to governed operation. This page highlights the three that most directly establish delivery and operating scope; the brief also covers managed AI-tool delivery and traceable knowledge preparation.
CONTROLLED CLOUD AI PILOT
Implemented the environment, tested the real access path, and enforced the gate.
Provisioned and configured an Azure AI Foundry pilot for a regulated engineering use case, obtained specialized data-handling and model-capacity approvals, and restricted access when validation exposed an external-search route outside the intended boundary.
Reviewed: environment records, participant-group corroboration, validation and gate records, third-party approvals, and the signed delivery baseline.
Verified boundary: exact configuration, user identities, production adoption, and service-level ownership are withheld or not claimed.
ENTERPRISE CHATGPT ROLLOUT
Deployed, migrated, and supported enterprise AI capabilities.
Deployed an internal GPT across the corporate workspace, supported migration of an early ChatGPT pilot used by a business team, published onboarding and usage guidance, and applied safeguards across a 100+ user administrative scope.
Reviewed: workspace deployment message, migration assignment, stakeholder acceptance and survey notification, administrative scope, leadership approvals, guidance, and support records.
Verified boundary: active-user count, costs, realized savings, quality change, and post-change adoption are withheld or not claimed.
A six-legged autonomous rover built with deterministic navigation, sensor-driven control, embedded firmware, simulation, telemetry, CAD, and fabrication.
31/32formal traversals
40/40simulation checks
7tested terrain categories
3COSGC recognitions
PRIVATE METHOD / PUBLIC-SAFE PROTOCOL
OBSERVE
Describe visible behavior without guessing at cause.
EXPOSE
Provide bounded code, logs, configuration, and state through MCP.
HYPOTHESIZE
Use Claude Code to connect physical symptoms to software paths.
VALIDATE
Accept only findings reproduced through engineering checks.
This diagnostic method is documented through a public-safe protocol; its private interaction history is not reproduced. The repository proves the robotics system and reported test results. It does not claim learned perception, trained policies, model inference on the rover, or AI-controlled actuation. Claude Code and MCP were used only for bounded diagnosis.
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