Everstone AI Blog

Most companies believe their data is AI-ready. They have a data lake. They have dashboards. They have tables with millions of rows. Then they point an AI agent at the warehouse and ask a simple question: “What was our churn rate last month?” The agent replies: “I cannot find a column named churn .” Or worse, it finds a column called CUST_STAT_CD , guesses that 0 means “churned,” and confidently reports a number that’s off by 50%. The problem isn’t that the AI is stupid. The problem is that your data is cryptic. For the last 20 years, we built data warehouses for human analysts—experts who rely on tribal knowledge to know that T_SALES_FINAL_V2 really means revenue. AI agents don’t have tribal knowledge. They only know what’s explicitly written in the schema. When metadata is missing, your data lake isn’t a lake. It’s a swamp. To fix this, you need a semantic layer . Here’s how to use Databricks Unity Catalog and Genie to teach your data to speak human.

There’s a fundamental difference between traditional software and AI software. When traditional software breaks, it’s loud. You get a “500 Internal Server Error,” an alert fires at 2 a.m., and an engineer deploys a fix. When AI software breaks, it’s often silent. The chatbot returns a fluent, confident answer that happens to be wrong. The user doesn’t open a ticket. They just lose trust—and quietly stop using the product. That kind of “silent failure” compounds over time. New data enters the system. User questions evolve. The world changes. And your AI drifts. I call this AI Rot . Most organizations try to solve AI Rot with anecdotes. They hold weekly meetings and trade stories like: “Someone in Marketing said the bot was wrong about Q3 sales.” That’s not engineering. It’s hearsay. If you want an AI product that lasts, you need a system where production failures automatically become regression tests. You need an agent that can heal—not by magic, but through disciplined feedback loops. ...