Spaces & chat analysis

A Space brings together datasets, business definitions, conversation history, and a default tier — it's where you do analysis.

Create a Space

1. Create a new Space;
2. Attach one or more datasets (create them first under Datasets);
3. Write business notes: table relations, metric definitions, field meanings, common filters, etc.

Business notes drive answer quality

Before generating SQL, the Agent consults your business notes to align "business language" with "data tables". For example, writing "in the orders table, status = paid means paid; GMV is the sum of paid order amounts" makes answers far more accurate.

Asking and following up

Just ask in natural language. A single answer usually includes:

• Reasoning: how the Agent understands the question and picks data and fields;
• SQL: the actual query, so you can verify it;
• Result table: the returned data;
• Chart: auto-generated from the question and result (see Dashboards & sharing).

Conversations keep context, so you can follow up on the previous result:

You: daily GMV over the last 7 days
Agent: (shows a daily GMV line chart)
You: now break it down by channel
Agent: (builds on the previous turn, querying by channel + day)

If a result is wrong, just say so (e.g. "exclude refunds from the amount") and the Agent re-queries.

Choosing a tier

You can pick a tier per conversation:

• Lite: simple aggregations and detail queries — fastest and cheapest;
• Standard: the recommended default for everyday analysis, balancing quality and cost;
• Flagship: complex multi-table reasoning, long context, and accuracy-critical cases.

Set a default tier per Space, or switch temporarily in a single conversation. See Tiers & pricing.

Asking in a group

Once IM is connected, you can @mention the bot in a group to ask, and the answer returns to the group. See IM integrations.

Mounting data in a general space

General assistant spaces can mount datasets too: click "Data" above the composer to pick tables, and the assistant gains a query_data capability (SQL runs server-side per dataset engine, up to 50 rows returned). Good for mixed "work + look things up" sessions; for the full analysis experience (charts, reports, trusted answers) use a data space.