Your data. A new perspective.

Turn connected data into clear decisions.

Bring your files, databases, and questions together. Explore with AI, uncover the story, and turn it into charts, dashboards, and analysis your team can build on.

Explore sample data Bring your own data

Ask one question now without an account. Sign in for 10 free charts a month — no card, and the sample data is already loaded.

Start where your data lives CSV & ExcelPostgreSQLSnowflakeBigQueryHTTP APIs Explore connections ↗

Less digging. More discovering.

A question in.
A new perspective out.

Follow the data from question to finding. Inspect the steps, adjust the chart, and keep asking.

twoHelixes / Revenue explorationSample analysis

↳How did revenue trend by region?

See the story behind the lines. In this illustrative dataset, East moves ahead of South in July. Rendered by the same chart engine used in the app.

One connected workspace

Go beyond the chart.

From a quick business question to hands-on analysis, work at the depth your decision needs.

Explore & explain

Ask in plain language. Compare performance, uncover trends, and inspect the transformations behind each result.

Explore a dataset ↗

Build a shared view

Bring charts into dashboards. Share a link with your team or export a figure for your next presentation.

Discover dashboards ↗

Go deeper with code

Work in SQL, sheets, and Python notebooks. Use agents for multi-step analysis, with editable code and reusable outputs.

Explore the toolkit ↗

Frontier reasoning. Inspectable results.

Muse Spark 1.3 powers the configured analysis route, with task-specific routing and fallback models. Chart validation stays in code, independent of the model.

Chart quality

Charts that do not mislead

The rules are enforced in code, not left to taste. Every chart is audited against them before you see it — including the three below.

Ranked categories, with the tail folded into Other rather than silently dropped.

A distribution, titled with the finding rather than the column names.

An area wash at 10% opacity — a hint of volume, never a saturated block.

What is enforced

Hues are assigned in fixed order and never cycled — a ninth series folds into Other rather than reusing a colour.
Scatter and map forms cap at three series, because past that no palette stays colour-blind safe.
Never two y-scales on one plot. Mismatched measures get their own chart instead of a fake correlation.
Light and dark are separately validated, not flipped.
Every finished chart is re-audited; anything that slips through is reported beside it.

Worked examples

21 datasets, already loaded, already answered

Every account starts with these. Each one has a page with its schema, its rows, and the charts the pipeline drew from them — with the reasoning behind every choice one click away. No account needed to read them.

Browse the datasets

Your data

Your sources. One place to make sense of them.

Drag in a CSV, Excel file or Parquet extract and ask a question thirty seconds later. Or connect the warehouse and leave the rows where they are — everything is read-only, and generated SQL is checked before it ever reaches a driver.

PostgreSQLMySQLSQL ServerSnowflakeBigQueryRedshiftClickHouseTrinoOracleDuckDBSQLiteMongoDBElasticsearchHTTP APIsCSVTSVJSONParquetExcelOpenDocument.gz.zip

orders.csv uploaded · 41k rows

warehouse · Snowflake read-only

regions joined on region_id, 98% overlap

SELECT … checked read-only

After the first chart

The chart is a starting point, not a screenshot

Change it for free

Swap the axes, the chart type, the grouping or the aggregation by hand and it redraws — no model call, no credit spent. Or say what you want changed in words and let the edit stage apply it.

Real Python underneath

The shaping runs in a sandboxed interpreter with pandas, polars, numpy, scipy, scikit-learn and statsmodels — so “fit a trend and forecast the next quarter” is a question, not a feature request. The code is shown and editable.

A real SQL editor

For when you already know the query. Schema-aware completions answer instantly without waiting on a model, plus generated SQL, per-source suggested questions and saved history.

Take it with you

SVG, PNG or CSV; pin it to a dashboard with a share link that needs no account; export the whole run as a Jupyter .ipynb or a marimo notebook — both run here as well as on your laptop; or call the same pipeline over HTTP.

Ask which channel brings the most signups?

Get started

Your next decision starts with a question.