A production-grade enterprise architecture blueprint demonstrating how Fortune 500 organizations can deploy governed, scalable AI natively on the Snowflake Data Cloud — from ingestion to inference, without data leaving the platform.
Click any component to explore its Snowflake-native implementation, SQL patterns, and enterprise design decisions.
Powered by the Anthropic API, these demos simulate how Snowflake Cortex LLM functions would operate natively in SQL — the same patterns I've architected and deployed at enterprise scale.
Governance is not a layer — it's woven through every tier. This framework ensures AI outputs remain trustworthy, auditable, and compliant at enterprise scale.
Hierarchical RBAC with functional roles mapped to personas. Row-access and column masking policies applied at the table level — transparent to consumers.
Automated column-level lineage via Snowflake Horizon. Every AI-generated output traces back to source tables, transformation logic, and LLM model version.
dbt schema tests + Snowflake Data Metric Functions run on every pipeline execution. Quality scores surface in Sigma dashboards; failures trigger Slack alerts.
LLM responses are stored with model version, temperature, and prompt hash. SHAP-style attribution links AI insights to underlying data signals — explainability by default.
Resource monitors at account, warehouse, and user level. Auto-suspend rules, query result caching, and materialization strategies minimize credit consumption.
Dynamic data masking for PII fields. Differential privacy patterns for sensitive aggregations. SOC2, HIPAA, and GDPR-ready architecture with audit logging enabled.
Reusable architectural patterns I've designed and deployed — each addressing a distinct enterprise AI use case on Snowflake.
Structured data (KPIs, metrics) flows from Gold layer through a prompt engineering layer to Cortex COMPLETE. Output is governed, stored, and surfaced in executive dashboards. Inspired by production work at Workday.
Internal documents, contracts, and tickets are chunked and embedded via Cortex Search. Users query in natural language; relevant chunks are retrieved and passed to Cortex COMPLETE — all within Snowflake's security perimeter.
Cortex Analyst exposes a semantic model layer — business users ask questions in plain English and receive verified SQL + results. Role-based filtering ensures each persona sees only their authorized data.
Snowflake ML Functions (FORECAST, ANOMALY_DETECTION) run directly on warehouse data — no model export, no MLOps overhead. Predictions land back in governed Gold tables for downstream consumption.