Selected work

Implementation evidence with visible claim boundaries.

These packages show architecture judgment, workflow thinking, and implementation artifacts. They do not turn sanitized patterns into client outcomes or prototypes into production proof.

Selected evidence

Inspect the work and the limits of what it proves.

Each record separates professional patterns, implementation evidence, public artifacts, and measured outcomes rather than presenting them as interchangeable proof.

Professional PatternDocumentedPublic ArtifactSanitized

Governed analytics delivery with Dataform and BigQuery

A sanitized professional pattern for making analytics promotion, validation, rollback, and cost checks inspectable.

Public result: The public artifact documents a repeatable governed analytics pattern and the boundaries required to operate it safely.

Claim boundary: This is a generalized professional pattern, not a named engagement, public deployment record, or measured customer outcome.

Open the evidence package

Professional PatternDocumentedPublic ArtifactSanitized

Compliant cloud data and ML platform design

A sanitized architecture pattern that treats compliance, analytics delivery, and ML feature access as one governed release path.

Public result: The public artifact shows how platform controls and delivery mechanics can be designed as one operating system rather than separate compliance paperwork.

Claim boundary: Illustrative timings and architectural targets are not reported as measured engagement outcomes.

Open the evidence package

Open Source ProjectPrototypePublic ArtifactPublic

Inspectable workflow orchestration with state machines

Open-source implementation work on Python statechart semantics, actors, clocks, and XState-compatible workflow definitions.

Public result: The public source and writing provide inspectable implementation evidence for an evolving workflow prototype.

Claim boundary: The repository is implementation evidence for an evolving prototype; it is not evidence of production adoption, durability, or operational outcomes.

Open the evidence package

Next step

Need this level of rigor around a live AI workflow?

Bring the workflow, its operating constraints, and the decision your team needs to make next.

Discuss a workflow