The Wren Journal Product, Tutorial
Claude can now build dashboards from a single prompt. Connected to Wren AI, it reads your context layer first, queries 20+ databases through one MCP connection, and every number on the dashboard links back to the SQL that produced it.
Wren AI Product Team
Updated: Oct 10, 2026
Published: Oct 10, 2026
Claude can now turn one prompt into a full dashboard. That changes the question a data team has to answer. It is no longer "can the model write SQL?" It is "would I put this dashboard in front of the CEO?"
The answer depends on what Claude is connected to. Pointed at a raw schema, it guesses what columns mean, picks its own joins, and runs with whatever access a shared service account has. Connected through Wren AI, it reads your context layer before it writes a single query, and every number it puts on the page traces back to governed SQL.
Here is the whole flow in 44 seconds.
20+ databases, one connector #
Most companies do not keep their data in one place. Sales sit in Postgres, finance in Snowflake, product events in BigQuery, and something important is still in SQL Server.
Wren AI gives Claude one MCP connection that reaches every source Wren AI supports: PostgreSQL, SQL Server, Oracle, MySQL, Snowflake, BigQuery, Databricks, Redshift, ClickHouse, Athena, Trino, Starburst and more, 20+ in all. There is no ETL step and no migration. The Wren AI engine plans each query against the source where the data already lives.
For Claude, that means a dashboard question does not stop at the edge of one warehouse.
A context layer Claude reads first #
Before Claude writes any SQL, it reads the model. In the demo, that is a retail project built around a Store Master table, connected to monthly POS sales, store KPIs, human resources, supply chain inventory, hourly sales, member RFM segments and new-store ramp-up.
The context layer tells Claude the things a column name never will:
- What a column means.
floor_area_pingis store floor area measured in ping, where one ping is about 3.3 square meters. Without that, "revenue per square meter" comes out wrong by a factor of three. - How tables join. Store Monthly KPI joins to Human Resources on
store_idandyear_month, not onstore_idalone. The relationship is declared, so Claude does not have to discover it, and every caller gets the same join.
These definitions live in MDL, Wren AI's context model, as git-versioned files in your own repository. Change a definition in a pull request and Claude uses the new one on its next question.
One question, 24 queries, one thread #
The prompt in the demo is one sentence: using the Mart dataset in Wren AI, build an executive dashboard for the CEO and CIO on the store expansion plan, clear, visual and boardroom-ready.
Claude breaks that into 24 queries and runs them through Wren AI's run_sql tool: member RFM segments, staffing and supply chain, revenue by trade area, format and city, ramp-up by month since opening, store-level positions, and more.
The interesting part is between the queries. Partway through, Claude notices that hard-coded months go stale and switches to a derived rolling 12-month window, then runs a query to test that window before relying on it. Later it goes back and verifies its numbers: format economics, network by month, trade area and format, store positions. Up to five queries run in parallel, and ten of them are kept as live sources behind the final dashboard.
That is what a context layer buys you. Claude spends its reasoning on the analysis, not on guessing what the data means.
Then it builds the dashboard #
The result is a Store Expansion Outlook that reads like something an analyst would bring to the board. It opens with a short narrative: transit-hub stores earn 25.2% more per month than the average store, a new store reaches full maturity in month 10, and the network is growing 3.5% year to date, so new doors should go where traffic is densest, in compact, high-yield formats.
Below that sit the headline KPIs, a four-part case for where, what, how fast and which cities to expand, revenue per store by trade area, and a trade area by store format heat map.
The demo's numbers come from a sample retail dataset. What carries over to your data is the structure: the dashboard runs on 10 live queries, and every number on it links to its SQL. When someone in the meeting asks "where does 153.1K come from?", the answer is one click away. It is not buried in a chat transcript.
Governed, not just generated #
A dashboard built by an AI model is only as trustworthy as the path the data took to get there. With Wren AI in that path:
| Claude on a raw database | Claude on Wren AI | |
|---|---|---|
| What columns mean | inferred from names | read from the context layer |
| Joins | chosen by the model | declared once, reused by every caller |
| Sources | one connection per database | one MCP connection, 20+ sources |
| Access | whatever the service account can see | row- and column-level security per user |
| Every number | a figure in a chat | linked to the SQL that produced it |
Access rules apply at query time to the person asking, so two people asking for the same dashboard see only the rows and columns each is allowed to see. Every request is logged with who asked and the SQL that ran.
Try it #
-
Add Wren AI to Claude. Search "Wren AI" in Claude'sconnector directory and authorize it with OAuth. Theconnector announcement covers setup in detail.
-
Run the open-source engine. Wren AI core isopen source on GitHub . You can run the whole path locally against your own database first.
-
Govern it at scale. Row- and column-level security and audit logs are part ofWren AI's paid plans . Claude builds the dashboard. Wren AI makes sure every number on it means what your business means.
See governed GenBI in action
Watch short demos of Wren AI turning business questions into governed SQL, charts, and reusable GenBI apps.
Related posts #
Wren AI Is Now Available in the Claude Directory
Wren AI is now listed in the Claude Directory. Add the connector inside Claude, authorize with OAuth, and Claude asks questions of your context layer instead of your raw schema, with row-level security and a full audit trail at query time.
How Wren AI turns a question into an answer
Ask in plain language. Wren retrieves the right business context, writes and validates the SQL, and runs it against your own database.
The Missing Context Layer for AI Agents Over Business Data
Why we rebuilt Wren Engine from a context layer into an open context engine, and what we learned along the way
The Wren Journal
Get the next deep dive in your inbox #
Practical GenBI guides, product updates, and customer lessons from the Wren AI team. A couple of emails a month, no noise.
Keep reading
Product, News
Wren AI Is Now Available in the Claude Directory
Wren AI is now listed in the Claude Directory. Add the connector inside Claude, authorize with OAuth, and Claude asks questions of your context layer instead of your raw schema, with row-level security and a full audit trail at query time.
Product
How Wren AI turns a question into an answer
Ask in plain language. Wren retrieves the right business context, writes and validates the SQL, and runs it against your own database.
Insight, Product
The Missing Context Layer for AI Agents Over Business Data
Why we rebuilt Wren Engine from a context layer into an open context engine, and what we learned along the way