Most teams building AI features aren’t blocked by the model. They’re blocked by the database underneath it: slow queries, no vector search, schema changes deployed by hand, pipelines nobody trusts. AI makes those problems visible faster than anything else does.

This site is where I’ll be writing about fixing that — the practical side of database modernization for AI-ready systems. Expect posts across a few recurring areas:

  • Indexing & performance — finding and fixing what’s actually slowing your database down
  • CI/CD for data platforms — treating schema and pipeline changes with the same rigor as application code
  • Data engineering — building pipelines that reliably get clean data where it needs to be
  • Vector databases & AI semantics — grounding AI features in well-modeled, retrievable data
  • The road ahead — including where quantum computing may eventually change how we think about databases

If you’re dealing with any of this in your own systems, or just want to follow along, subscribe to the feed or get in touch.