— Blog · The Discipline Layer for AI
Contrarian, data-grounded essays on the three things that decide whether AI pays: a sharper problem, governed data, and demand that survives the answer-engine age.
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Growth · Featured essay
The first content strategists painted on cave walls. The newest ones orchestrate agents. This essay traces the whole arc — and shows how a disciplined content engine drove 359% organic traffic growth for a B2B client by treating generation as a system, not a shortcut.
Frank Shines & Umer Qureshi · Read the essay →
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Every post strengthens one of the three foundations — Problem, Data, or Growth.
A practical AI governance framework a mid-market team can run on Unity Catalog and the lakehouse, mapped to the NIST AI RMF functions and to the evidence several new rules ask for.
PCI DSS v4.0.1 expects automated audit log review. Here is how to build it on a lakehouse, from ingestion and OCSF normalisation to daily detection and the evidence table your QSA will sample.
Yes, the EU AI Act can apply to a US company with no EU office. Here is what triggers it, which obligations already apply, the corrected high-risk dates, and the data evidence to build first.
A side-by-side reference for audit log retention across PCI DSS, SEC rules, HIPAA and the new AI laws, with the legal text kept apart from the engineering recommendation and one lakehouse design that covers them all.
Databricks renamed Vector Search to AI Search. Here is how its indexes, endpoints and sync modes work for RAG on the lakehouse, and how to tell if you are ready.
Agent Bricks turns governed lakehouse data into evaluated, deployable agents. Here is how it works, what its status really is, and how to ship one.
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