Most data teams build for the demo. I build for what runs after it.

Governance teams get audit-ready accountability frameworks aligned to NIST RMF, EU AI Act, and ISO 42001. Operations teams get infrastructure that handles real pipelines at production scale. Everything here is live.

Moving past simple model audits to operationalize accountability, identity tracking, and socio-technical safety thresholds within deaf and hard-of-hearing accessibility technology. Covers audit lineage, risk thresholds, and accountability models aligned to NIST RMF and EU AI Act. Remapped from foundational governance scaffolds to real, high-stakes outcomes.

Python   Streamlit   NIST RMF   EU AI Act   ISO 42001

Treating data as a product for internal stakeholders. Built on a 135-deal pipeline, 480-row multi-service-line revenue dataset, and 12-rep performance model. Four-tab application covering pipeline health, risk-adjusted forecasting, multi-motion revenue analysis, and compensation modeling. Forked from an existing scaffold, stripped of infrastructure dependencies, rebuilt with a CSV-native data layer and Sorenson-specific analytical logic.

Python   Streamlit   Plotly   pandas   NumPy

Most AI governance tools hand you a list of regulations. This one tells you which ones apply to your organization. Built on a 4-stage elicitation protocol and jurisdiction routing logic across 40+ frameworks in 15 jurisdictions. Six-tab output covering jurisdiction applicability, obligations, cross-cutting themes, action plan, and sources register. Original build, no scaffold. Scenario: SignalPath Technologies, biometric data processing across US, EU, UK, and Canada — a compliance trigger that cannot be engineered away.

React   Vercel   EU AI Act   NIST AI RMF   GDPR

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Building transparently. Shipping production systems. Securing automated behavior.