The Six‑Tier Enrichment Cascade: How I Stop “Helpful” Data From Overwriting True Data

I noticed something that felt impossible at first: the more enrichment I added, the more often my records got worse . Not catastrophically. Not in a way that threw errors. Just… quietly. A city would flip to something nearby. A state would “correct” itself. A phone number would appear, but it was clearly the corporate HQ line, not the local office. And the worst part was that every one of those changes looked superficially reasonable—exactly the kind of “helpfulness” that sneaks past review. This is Part 3 of “How to Architect an Enterprise AI System (And Why the Engineer Still Matters)” . In Part 2, I showed why I accept variance upstream and then validate hard downstream. This post is the other half of that philosophy: when you enrich data from multiple sources, you need a system that can accumulate truth without regressing it . The core decision: I built a six-tier enrichment cascade with per-field provenance tracking . Each tier is allowed to fill blanks, but it is not allowed to o