Regulatory, compliance & AI governance
Rules as competitive infrastructure — across materials regulation, privacy, and AI governance.
Regulation is often the schedule and the moat — whether the topic is producer responsibility for packaging, GDPR/CCPA for a data platform, or emerging AI governance for a model deployment. I work from primary sources and connect them to product, pricing, evidence, and engineering so compliance supports the commercial story instead of arriving late.
What you get
- ▸Primary-source analysis on EPR, climate disclosure, food-contact, and packaging regimes (analytical, not legal advice)
- ▸Compliance engineering, AI governance, and privacy work: GDPR / CCPA / HIPAA-adjacent data flows, model cards, evaluation harnesses, and disclosure-grade audit trails translated into operable engineering requirements
- ▸Testimony and advocacy support where public rulemaking affects category definition
- ▸Disclosure-grade analytics infrastructure (claim validation, evidence bundles, lineage)
- ▸Procurement-ready documentation paths aligned to buyer questionnaires and AI risk reviews
- ▸Early identification of binary gates before capital, model launch, or line commitments
Example work
- EcoMetrics
Climate intelligence: validate environmental claims for 75+ SKUs. dbt pipeline with 105+ data quality tests, DuckDB analytics, Streamlit dashboards — transaction-level logic, monthly aggregation models, multi-company architecture.
Related
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Selective conversations with founders, operators, and diligence teams. Mention regulatory strategy, compliance engineering, or AI governance in your note so we can route quickly.
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