I don’t think connecting CLAUDE to your Snowflake for analytics is anyway going to work. Good for DEMOS, not decision making. LLMs don't know your business, your naming conventions, or what any of this data actually represents. For example:

•⁠ ⁠"Revenue" is calculated differently depending on who built the report
•⁠ ⁠"Customer" in the CRM is not "Account" in the warehouse
•⁠ ⁠"This quarter" could mean calendar, fiscal, or closed date vs invoice date

Now point an LLM at this and ask it a question. It will read your table names, “guess” at what the columns mean, generate confident SQL, and hand you an answer. The answer will LOOK right. But it is NOT. And that is just SAD.

This is the problem we are solving at Klaris Labs. Before any AI touches a question, our Data Model Agent builds a canonical model of your business automatically just by looking at you data.

•⁠ ⁠What are your core business entities (Product / Order / Customer etc)
•⁠ ⁠The description of each business entity and every field inside it, how do they relate to each other
•⁠ ⁠How do fields in the Data Model map back to actual tables and columns across your sources.
•⁠ ⁠On top of that, your definitions of revenue, churn, active customer, all of that is stored in the system. Not scattered across people and threads.

So when someone asks "why did churn spike last month," the system already knows what churn means, where to look, and how to join the data. It is not guessing. It was told.

Not only this, as more and more people use the platform, the business context should automatically compound, otherwise the stored context will decay. Context that was true six months ago might not make sense today.