Trusted answers for agents, not just dashboards

There's a demo everyone has seen by now: point a chatbot at a database, ask it a question, watch it write SQL. It's genuinely impressive — right up until you ask something that matters and quietly get a wrong number.

The problem isn't the model. The problem is what you handed it. A bare SQL endpoint gives an agent table names and column types and nothing else. It doesn't know that revenue is net of returns, that one entity's chart of accounts differs from another's, or that intercompany transactions need eliminating. So it guesses — fluently, confidently, and sometimes wrong.

In a consumer chat app, a plausible-but-wrong answer is a shrug. In a business, it's a board deck with a number nobody can defend.

Grounding beats cleverness

The fix isn't a smarter model. It's better context.

When an agent asks gatlas a question, it isn't handed raw tables. It's handed a semantic layer: named views with definitions, the AI context an analyst would give you, and column-level docs. "Gross margin" means one specific thing, computed one specific way, the same for every entity. The agent doesn't infer the business — it reads it.

And every answer carries its receipt. Grounded in · Consolidated Portfolio P&L. You can see which view a number came from, so you can trust it or challenge it.

Governed, because it has to be

Trust isn't only about accuracy — it's about boundaries. An agent should see exactly what its key allows and nothing more. So access is scoped to specific entities, specific views, specific tools. Read-only by default. Every query logged.

That's what makes it safe to put an agent in front of real financial data: not blind faith in the model, but a layer underneath it that's documented, scoped, and observable.

Ask from Claude, over MCP, in SQL, or from Slack. The surface changes; the trusted answer doesn't.

This is an early draft — we'll keep sharpening the argument as the product and the field evolve.