Enterprise AI Architect | Data-Intensive Workflows

I architect and build data-intensive AI workflows that teams can operate, evaluate, and decide to scale. My work closes the gap between a working demo and a dependable production system by making source authority, human review, failure handling, observability, and release evidence explicit.

Portfolio | Project catalog | Public evidence standard

I work with teams in regulated and data-intensive environments where a wrong, late, or unsupported result has an operational cost. I connect data and platform architecture, AI and agent behavior, workflow state, evaluation, and product delivery so the system can be trusted by the people responsible for running it.

My primary tools include Google Cloud, BigQuery, Snowflake, Azure, SQL, Python, TypeScript, and Go. I approach architecture as a product and operating discipline: technical choices should support a defined business decision, the people accountable for it, and a maintainable path through production.

• Measured Studios is my independent consulting studio. A two-week AI Workflow Value and Readiness Sprint tests whether one high-value workflow justifies a production pilot. A six-to-ten-week Governed AI Production Pilot implements the approved slice inside the client's environment.
• Rehearsal is a private-source research prototype with two synthetic workflow scenarios and a shared deterministic kernel. It generates inspectable hypotheses; it does not establish customer adoption, external calibration, or real-world outcomes.
• Mainland Dispatch is a separate public research lane for contextual China and U.S.-China coverage. It is evidence of research method, not consulting delivery or customer impact.

The public project catalog keeps project type, evidence context, maturity, evidence level, confidentiality, business model, distribution mode, and runtime state separate. Repository activity, deployment, commercial intent, and customer outcomes are not treated as interchangeable proof.

Browse all repositories.

• Enterprise AI workflow architecture and implementation
• Data authority, platform integration, and state ownership
• Agent evaluation, human review, and escalation design
• API, event, and state-machine contracts
• Production observability, security, release, and cost controls
• Technical strategy translated into testable product and operating decisions

• Prefer evidence over implied readiness: source, tests, CI, deployment, and live behavior are separate claims.
• Make contracts executable at system boundaries through schemas, validation, tests, and observable failure modes.
• Keep changes reproducible, dependency-aware, and small enough to review without losing the larger architecture.
• Build tools around the teams and decisions they serve, not around technology for its own sake.

This profile repository has no runtime dependencies. Its standard-library validator protects the README structure, link syntax, canonical GitHub targets, relative files, and duplicate-link contract.

python3 -m unittest discover -s tests -v
python3 scripts/validate_readme.py README.md

The validator does not claim that remote content is current or available. External links and project descriptions still require manual review when they change.

This repository is available under the MIT License.