Tag archive
A data quality agent is software that watches your warehouse, investigates problems, and explains them on its own, instead of making you check a dashboard. Here's what that means, how it differs from rule-based monitoring, and why the category showed
Google's Open Knowledge Format (OKF) formalizes the "LLM Wiki" pattern: give an agent a curated markdown knowledge base instead of RAG over raw chunks. We've run a variation of that for warehouse context since 2025. Here's why a living knowledge base
AI agent projects are really data projects. Why data preparation and upkeep, not the model, decides whether an agent ships, and how to scope the data work first.
Learn how to assign real numbers to CRM data quality using proven metrics, scoring frameworks, and industry benchmarks that reveal where your pipeline data actually breaks down.
Bad data gives no warning. An age of 200 years, a duplicate customer number, a country code that...
Discover the key to building efficient AI agents by following these expert-proven tips and tricks to revolutionize your AI model optimization.
Mexico's August 1 electronic customs value rules expose a problem Canadian importers face under CARM: inconsistent commercial data across invoices, origin certificates, and freight manifests triggers
One Field, Multiple Downstream Systems For anyone building or managing trade compliance...
60% of businesses cite poor data quality as the primary reason for AI failures. Dirty data leads to...
Originally published at norvik.tech Introduction Explore the insights from Margaret...
Discover the common pitfalls in AI-powered development and learn how to harness its power with our expert tips and tricks.
Automated lifecycle stage transitions save hours of manual work but can silently corrupt your pipeline reporting. Here is how to build them safely.