These are live, operational systems — built, deployed, and operated by one engineer. Each demo opens in a dedicated environment. Interact with real data, real APIs, real results.
Point it at any public GitHub repository and receive a scored governance compliance analysis in under 30 seconds — deterministic pattern analysis against real source code, no mock data, no AI-generated guesses. The scanner is one module inside the unified v3 governance cockpit: project onboarding, PMO dashboard, governance scorecard, gate manager, artifact generator, and a Framework Explorer covering NIST AI RMF, ISO 42001, EU AI Act, HIPAA, GDPR, and SOC 2. v3 adds a runbook library — DevSecOps, PM/Scrum, Workshop Guide, Policy as Code, and Software Factory — so every AI-generated commit is scanned against the same rule set as human-written code.
Multi-agent AI pipeline that scores an enterprise application portfolio against cloud migration criteria and produces auditable, per-app recommendations — dependency mapping, ROI math, ISV/EOL evidence, and a provenance table showing exactly which deterministic rule validated (or overrode) each AI recommendation. Paired with an interactive Digital Twin built on the same masked healthcare engagement data: a force-directed dependency graph exposing hidden cross-stack risk, and a 5-wave migration scenario simulator with TCO modeling, so stakeholders test migration waves before committing instead of discovering the risk in production. Every number traces back to a governance rule, not a model guess. Details masked for client confidentiality.
A premium add-on to the Governance Platform and MemoryOS: an AI code-generation pipeline — Ideation → Design → Development → Deployment → Operations — that writes application code against a build spec already encoding the applicable governance requirements, so the output is scanned-clean by construction, not by luck. Every AI-generated commit runs through the same scan engine as the Code Scanner above, and Refactor/Rebuild recommendations from the App Rationalization engine feed directly into this pipeline as its entry point. Consolidates two previously separate, working codebases — a 6R/7R rationalization engine and a discovery-to-audit build pipeline orchestrated with LangGraph. Governance Scanner and MemoryOS integration are still in progress, and no production deployment has shipped yet.
A new enterprise data AI pipeline design: connects to just about any enterprise data source — chat, ticketing, wikis, document repositories, email, or a custom pipeline — through an extensible connector library, then runs every document through a 4-stage Bronze → Silver → Gold enhancement pipeline before it ever reaches a search index. Bedrock Guardrails and Claude are already the production AI layer today, called from Azure-hosted infrastructure — this page documents the full AWS-native port target, a design spec rather than a deployed system.
A location-aware generative AI product: drop a GPS pin anywhere and get an AI-narrated historical brief sourced from 60M+ digitized public-domain records (Library of Congress, National Archives, Smithsonian, DPLA) — tuned to 5 distinct audiences from casual visitors to K-12 educators and tourism boards. Built to qualify for NEH / IMLS cultural-heritage digital-access grant funding.
A full token audit of a 7-agent enterprise AI rationalization pipeline — real token counts from AWS Bedrock Converse API usage fields, not estimates. Per-agent cost breakdown, retry economics, prompt anatomy, and a prioritized optimization roadmap across a 36-application healthcare portfolio run. Shows $23.97 baseline path to $7.63 with three targeted changes.
How I approach enterprise application architecture, shown across the apps on this page — several of which are built, or actively being ported, across more than one cloud. Every component is a managed service chosen for compliance, zero operational overhead, and pay-per-use economics, with explicit cost and data-path rationale documented per cloud.
A full-stack AI platform for theme park intelligence. Real-time crowd prediction, sellout event forecasting, satellite weather analysis, ML ride-wait models, and daily AI-generated social content — all running 24/7 on a multi-cloud stack across Azure, GCP, and AWS. Built and operated solo under $100/month.
These are working systems, not slides — and I take on a limited number of freelance engagements through AI Pathfinder LLC to build the same kind for other teams. If one of these demos is close to your problem, let's talk about yours.