Built a controlled cloud AI pilot, found an external-search path during validation, and restricted expansion until a safer design could be established.
ROBERT GANEY / AI SOLUTIONS LEAD
I lead AI solutions from strategy and architecture through engineering delivery.
I turn ambiguous business, research, security, and export-control needs into AI architectures, governance controls, technical plans, and decision paths that engineering teams can implement. My work spans enterprise AI platforms, evidence-governed software, MCP integrations, and autonomous robotics.
A public, runnable reference system with provenance, invalidation, audit trails, deterministic evaluation, and MCP.
Plus 40/40 simulation checks for an autonomous six-legged rover.
SELECTED WORK
From enterprise decisions to field-tested autonomy.
I connect strategy, controls, software, infrastructure, and physical systems. Each case states the result, my contribution, and the evidence available.
Regulated AI solution leadership
RESULT
Built a controlled AI pilot, found an external-search path outside its intended boundary, and restricted rollout pending a safer design.
The work translated research, business, security, access, and export-control constraints into a defensible system boundary, a working cloud AI environment, and an evidence-based release decision.
CONSTRAINT
Enable advanced AI use without blurring controlled-data, access, residency, or operational boundaries.
MY CONTRIBUTION
Led the technical architecture and decision planning; provisioned the environment; implemented identity, access, and least-privilege controls; secured specialized vendor approvals; tested the real user path; restricted access after validation exposed an external-search route; and advanced the work into a signed cross-functional baseline.
VERIFIED OUTCOME
Private implementation and Teams records support a working pilot with restricted participant access and a real gate decision. Third-party records verify specialized control and model-capacity approvals, and the cross-functional plan received all required signatures. Production adoption, service levels, and business impact are not claimed.
01Requirementsresearch / business / security
02Boundaryidentity / data / resources
03Controlsaccess / keys / logging
04Deliveryowners / gates / dependencies
Evidence governance for AI systems
Built a local governance application whose MCP interface lets an AI client draft recommendations from approved evidence while keeping every source visible and every final decision with a person.
FLAGSHIP PUBLIC SYSTEM
AI can draft the work. It cannot make the decision.
ChangeFront stores approved source material, tracks when it changes, and shows exactly which evidence supports each recommendation. A human must approve or reject the result.
ENGINEERING PROOF
Designed to be run, inspected, and challenged.
The source package includes one-command setup, architecture and trust boundaries, a repeatable evaluation harness, audit traces, failure cases, deterministic tests, and a real stdio MCP interface. It contains no employer code or data.
Enterprise AI operations, governance, and enablement
RESULT
Moved enterprise AI work into a governed operating path through deployment, migration, safeguards, exception handling, and vendor diligence.
Workspace operations
Implemented ChatGPT and Codex safeguards across a 100+ user administrative scope, then published onboarding, usage, and efficiency guidance used in employee support.
Rollout and migration
Deployed a workspace-wide internal GPT and supported the move of an early business-team ChatGPT pilot into the corporate environment, with documented stakeholder acceptance.
Release and exception controls
Turned leadership decisions into practical usage boundaries and a leader-approved review path for business-critical work that standard limits would block.
Governance and vendor diligence
Scoped inventory, controls, ownership, and success criteria while leading diligence across data residency, key management, monitoring, API governance, and contractual controls.
Status: private email, Teams, admin, support, and vendor records verify the workspace deployment, accepted business-team migration, 100+ user administrative scope, implemented safeguards, published guidance, approved exception design, vendor diligence, and scoped governance discovery responsibility. Realized savings, adoption change, direct reports, budget authority, and a formal AI Solutions Lead title are not claimed.
Physical AI foundations and autonomous robotics
RESULT
Co-developed a six-legged autonomous rover using deterministic navigation and gait control, embedded sensing, simulation, and measured field validation.
My work crossed Python navigation and gait control, Arduino/C++ sensing, telemetry, simulation, CAD, fabrication, electronics, and regression testing. The public project demonstrates autonomous robotics and AI-assisted engineering, not learned perception or model-based control.
31/32formal traversals
40/40simulation checks
7terrain categories
3COSGC recognitions
ENGINEERING SCOPE
One system across software and hardware.
Co-developed the Python gait and navigation stack, integrated Arduino/C++ sensing with Raspberry Pi control, built simulation and telemetry tooling, and owned the CAD and custom fabrication workflow.
PRIVATE METHOD / CLAUDE CODE + MCP
Physical symptoms, inspectable software context.
I used MCP to pair observed rover behavior with bounded code and telemetry context in Claude Code. The assistant produced hypotheses; the rover remained under deterministic software control, and findings were accepted only after simulation or field checks.
HOW I WORK
Evidence before adjectives.
01Translate constraints
Turn ambiguous business and risk needs into explicit technical requirements.
02Design the boundary
Define architecture, access, ownership, dependencies, and acceptance gates.
03Build the asset
Leave behind reusable tooling, guidance, code, or implementation plans.
04Verify the claim
Use deterministic checks, field evidence, and accountable human review.
ROLE FIT
AI leadership grounded in architecture and engineering.
My work spans two kinds of complexity: enterprise AI decisions shaped by security and governance, and hands-on engineering where software has to work on real hardware. That combination lets me lead architecture and delivery without losing sight of how the system will be built, tested, and supported.
I am targeting AI Solutions Lead roles that combine strategy, platform and model decisions, governance, and engineering delivery across software, integrations, and physical systems.