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How Moodree works,
and what it unlocks.

What Moodree actually does, why the foundation has to come first, why all of it runs in a Google Cloud project you own, and the four ways your team gets at the data once it is in place.

What Moodree does

Meet Moodree, your AI data team.

Your data engineer, architect, and analyst. In one product.

Moodree is an AI layer on top of Google Cloud. It connects your sources, designs your warehouse architecture, generates tested documented Dataform models, and proactively tells you what matters before you think to ask.

DATA ENGINEERING

Sources connected. Data flowing.

Your data engineer, automated. Moodree extracts data from all your sources on schedule and keeps everything running reliably.

✓Automated data ingestion from any source
✓Incremental pipeline setup if necessary
✓Error handling with plain-language explanations
DATA ARCHITECTURE

Warehouse built around your business.

Your data architect, automated. Moodree generates your entire warehouse structure, keeps it documented and ensures you always trust your numbers.

✓Medallion architecture, models and pipelines
✓Automated data quality tests and lineage
✓Automated documentation and business context
DATA ANALYTICS

Answers and real actions, not dashboards.

Your data analyst, automated. Moodree monitors your warehouse around the clock and proactively tells you what matters before you think to ask.

✓Proactive analytical agent, 24/7 monitoring
✓Anomalies and insights surfaced automatically
✓Consumption layer ready for dashboards, AI agents and data apps
Why the foundation comes first

The people who build Claude
had to build this first.

Anthropic pointed Claude straight at their own data and measured how often it came back with the right answer. It was right 21% of the time. Not because the model was weak, but because raw tables carry no meaning.

So they built the layer underneath. The same model then reached 95%, and today handles most of their analytical questions without an analyst.

That layer is exactly what Moodree builds, in about a week. Read their write-up.

21%95%
Correct answers, same model
  • Canonical datasets
  • One definition per metric
  • Business context written down
  • Validation before anyone acts
Why Google Cloud

It all runs in a project
you own.

Your warehouse, your models and your pipelines all live in a Google Cloud project you own, built only from native services: BigQuery, Dataform, Cloud Run and Workflows. Moodree builds and maintains them, but your data never sits on our side.

AI connects straight to the warehouseBigQuery has an AI agent and an MCP server built in, so Claude or Gemini answer from the warehouse itself, not from an export or a copy.
Google's own tools connect themselvesGA4 and Google Ads export into BigQuery natively. No connector to buy and no pipeline to maintain for the sources most companies start with.
Every change is reviewed and reversibleYour models live in a Git repository, so nothing is buried inside a tool's interface and you can always see who changed what, and why.
Cheap to run, and it grows with youServerless and without licences: you pay for what you actually use and the cost is visible down to a single query. As volumes grow there are levers to pull, not a platform to replace.
Tools your team can hire forBigQuery and Dataform are industry standard. Any data person already knows them, which matters more than it sounds the day you need to add someone or replace them.
Room to build on topData apps, new use cases and smaller side projects all run on the same foundation, in the same project.
What this unlocks

One foundation,
four ways to use it.

Everything below reads the same models and the same metric definitions. Nothing recalculates anything on its own, so the number is the same wherever you look at it.

The insight comes to you

Moodree News goes through your warehouse every night, checks what it finds against your business context and sends what actually moved. You read it instead of querying for it.

Daily digest · part of Moodree

Dashboards in the tool you already use

Your BI tool connects to the governed presentation layer and only reads finished numbers. No logic lives in the dashboard, so two reports cannot disagree.

Looker Studio · Metabase · Power BI

Questions answered directly by your data

BigQuery ships with a built-in data agent and a native MCP server, so Claude or Gemini can query the warehouse in plain language and answer from approved numbers, with the definition behind each one.

Claude · Gemini · MCP

Data apps built in a few prompts

Not just charts: a client lookup, a margin calculator, an ops screen made for one team. Built on the same definitions and hosted in your own Google Cloud for a few euros a month, with no licence per user. Access rights from the warehouse carry over and people sign in with your Google Workspace.

Cloud Run · your own GCP project

The only question left is
what we connect first.

A 30-minute call. Tell us which tools you run on and we will sketch what your foundation would look like, and what it would open up.

Book a free call → See pricing