Everstone AI Blog

 There’s a common pattern in enterprise AI: users ask the same questions again and again. Think about your internal knowledge base. Employees constantly ask things like: “How do I reset my VPN?” “What are the Q3 sales figures?” “What’s the company policy on expense reports?” A standard RAG system has no memory of these past interactions. Every time a question comes in, it repeats the same expensive steps: Embed the query Search the vector database Retrieve the top documents Send everything to the LLM (GPT-4, Llama, etc.) Generate an answer Even if the question—and the answer—was identical five minutes ago. This is incredibly wasteful. You’re paying for retrieval and inference on every request, even when nothing has changed. It’s like running a factory production line just to print the same invoice twice. The result is predictable: rising costs and unnecessary latency. The fix is semantic caching —a technique that recognizes repeat intent and serves answers instantly, without re-run...

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.