Short, specific pieces on entity resolution, industry AI in practice, enterprise privacy architecture, and lessons from Fabric, Databricks, and Snowflake engagements. No filler, no generic AI takes.
Most MDM platforms were designed before AI-native matching existed. Here's what that costs enterprises today, and what actually changes with an AI-first approach.
Rules-based MDM was built for data that doesn't change shape. Enterprise data changes shape constantly. Here's what that costs, and how machine-learned resolution closes the gap.
Sending every record through the most expensive resolution method is a common and costly design mistake. Here's how tiered escalation changes the cost curve.
Entity matching and relationship traversal are different computational problems. Forcing one engine to do both slows both down.
Multi-agent AI systems don't stay reliable or affordable by accident. Here's what actually governs cost, behavior, and drift in production.
"We don't train on your data" isn't a privacy architecture — it's a sentence in a sales deck. Here's what a real single-tenant boundary looks like, and the questions to ask any vendor claiming one.
A field-level look at what changes when AI is introduced at first-line review — and the parts of the workflow best left alone.
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