AI Architect · Austin, Texas · Website · Email · LinkedIn · X
I design agentic systems where a model can propose and cannot grant itself permission. Financial services trained the consequence. Founding trained the ownership. The public repos are the parts a reviewer can inspect.
Current: Founder, Apex AI|ML. Prior: Minotaur Consulting · Office of the CIO · Wall Street.
| Order | Repository | Sector | What it Proves |
|---|---|---|---|
| 01 | Agent Foundry | Enterprise AI | Capability, identity, and runtime grants are separate. 399 tests. AWS DEV. |
| 02 | Monster Heavy | Capital Markets | Durable paper execution survives retry, concurrency, and worker death. |
| 03 | AI-Ready Data Platform | Retail Analytics | Three specialists. Claims checked against bounded facts. |
| 04 | Capital Markets Research desk | Energy Trading | Massive historical evidence, fixed-rule holdout, and checked CrewAI research. |
| 05 | Investment gems | Suggestion Engine | Explicit momentum, volatility, and liquidity hurdles; bounded CrewAI challenge. |
| 06 | Paper trading floor | Multi-Agent MCP | Local human confirmation, independent account checks, and persistent simulated fills. |
| 07 | BALLAST | Supply-Chain | Explainable supply-chain risk, what-if shocks, and a disruptive-action approval gate. |
| 08 | AEGIS Evidence | Cyber-Security | Replayable SOC assurance workpaper, human acceptance, open gaps, and SHA-256-verifiable archives. |
These eight projects show depth in governed AI and breadth across capital markets, energy, supply chain, and cybersecurity assurance. BALLAST makes operational trade-offs visible; AEGIS Evidence makes governance claims and unresolved control gaps inspectable.
Monster Light and Monster Desk remain supporting paper-boundary examples alongside Monster Heavy.
A recommendation is not permission. Approval is bound to the exact action. Current evidence is checked again at execution. If the control cannot be shown, the claim is not made.
Three interactive local applications combine Massive historical daily market evidence, Python-calculated metrics, and bounded CrewAI research. Structured claims are checked against the supplied evidence; interpretation still requires human review. Agents have no order tools. Only the paper floor records simulated fills, after explicit local confirmation and account checks.
- Capital Markets Research Desk pairs a research brief with a fixed-rule chronological holdout, benchmark comparison, and visible cost assumptions.
- Investment Gems applies transparent momentum, volatility, and liquidity hurdles to a selected equity/ETF universe.
- Paper Trading Floor retains simulated cash, positions, and an evidence-linked SQLite fill ledger across restarts.
Each repository includes screenshots, setup instructions, tests, and explicit limitations. Data are end-of-day historical observations; price returns exclude dividends. Energy equities and ETFs are proxies, not ERCOT power or Henry Hub spot feeds. There is no broker connection or real-money execution path. The original static fixture views remain documented as legacy examples.
Evidence scope and confidentiality
Confidential client material is not published. Public FinOps repositories contain modeled scenarios with documented formulas and assumptions; their dollar figures are not delivered client savings.
Senior seats in AI architecture, enterprise AI, cloud and AI platforms, technical consulting, and technical chief of staff. Austin, Dallas, Houston, or San Antonio. In office or hybrid.
Close enough to build. Senior enough to say no.