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AI & Data Notes
Separating AI hype from practical utility. Real-world insights on analytics, database pipelines, and how modern organizations can leverage intelligent systems responsibly.
AI & Data
Practical Data Thinking
Artificial intelligence is only as strong as the foundational data architecture supporting it. Too many initiatives stumble because of unclean data models, fragile ingestion pipelines, or lack of business alignment.
My focus is on pragmatic AI: building high-integrity relational and analytical foundations, designing clean retrieval workflows, and implementing solutions that deliver quantifiable value rather than expensive experiments.
Core Focus Areas
- Data modeling for operational vs. analytical workloads
- Pragmatic LLM integration & retrieval augmented generation (RAG)
- ETL/ELT pipeline optimization and data verification
- Governance, privacy, and cost containment in cloud analytics
Consultation
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