Claude는 대쉬보드를 만듭니다. Malloyyo는 숫자가 맞습니다.

Claude는 건물 대시보드에 정말 좋습니다. 데이터 (MCP를 통해)에 액세스하고 몇 분 안에 아름다운 Claude Dashboard를 구축 할 것입니다. 그 부분은 해결됩니다.

누락된 부품은 다른 모든 것입니다: semantics, access control, the shared home for the 대쉬보드, 그리고 작업 흐름을 관리하기 위해 더 많은 사람들이 같은 데이터에 구축.

그래서 우리는 그것을 만들었습니다. Malloyyo는 Claude 또는 웹에서 "Claude Dashboards"를 제공합니다. Malloy semantic 모델의 데이터의 의미를 관리합니다. 역할 기반 보안을 사용하여 무엇을 볼 수 있는지 제어합니다. 그리고 GitHub 저장소의 대시보드와 모델을 유지하므로 모두 확인 및 유지가 가능합니다.

데모 서버를 사용해보십시오. Google 계정으로 로그인하십시오.

데이터 chaos, 지금 10x

Looker는 data chaos라고 합니다. 두 사람이 회의로 걸어, 각각 자신의 분석. 숫자는 일치하지 않습니다. 인수 시작.

AI를 추가하고 문제는 10 배 더 악화됩니다. 분석은 지금 싸다. 10명이 회의로 걷고 있으며, 각각 vibe-coded 대시보드와 숫자 일치하지 않습니다.

두 번째, 더 어두운 문제가 있습니다 : ** 액세스 제어 **.

Malloyyo

Claude builds the dashboards. Malloyyo makes sure the numbers are right.

Claude is really good at building dashboards. Give it access to your data (via MCP) and it will build you a beautiful Claude Dashboard in a few minutes. That part is solved.

The missing parts are everything else: semantics, access control, a shared home for the dashboards, and a workflow to manage it all as more and more people build on the same data.

So we built it. Malloyyo serves your “Claude Dashboards” in Claude or on the web. It manages the meaning of your data in a Malloy semantic model. It controls who can see what using role based security. And it keeps your dashboards and your model in a GitHub repo, so all of it can be verified and maintained.

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Data chaos, now 10x

At Looker we called it data chaos. Two people walk into a meeting, each with their own analysis. The numbers don’t match. An argument starts.

Add AI and the problem gets ten times worse. Analysis is cheap now. Ten people walk into the meeting, each with a vibe-coded dashboard, and none of the numbers match.

There’s a second, darker problem: access control. The database is the wrong layer for deciding what people can see. A table lands in the wrong place. A permission is missing. Queries draw from lots of places, and it’s very hard to know exactly what any one person can reach. The same goes for an AI.

A semantic layer solves both.

• Numbers match ✓ The model defines the calculations and the join relationships once, Anything built on those calculations is correct.
• Access control ✓ You grant access to a semantic model, which is a much bigger and more meaningful unit than a table. The AI can only query what the model exposes.

“Do we really need a semantic layer with AI?”

People want a yes-or-no answer. Ours is “Yes, and…”

I think the real reason people say “no” is the end-state problem. A traditional semantic layer describes calculations and joins, which is great, but a query against it is just a set of parameters. When the calculation you need isn’t in the model, you’re stuck. You download the data and finish the job somewhere else. A business user opens a spreadsheet. An AI opens Python.

Either way you’ve left the governed system, and the numbers start to drift.

When we designed Malloy, we decided the semantic layer and the query language had to be one system. Malloy describes the semantics, and it’s also a complete query language. Any question you can answer in SQL, you can answer in Malloy, scoped to the model. You get consistent definitions and bounded access, and you never hit the end state.