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For questions with specific metrics, fields, and time ranges, the AI Analyst uses Standard Analysis — a streamlined workflow that quickly retrieves your data and generates either an analytical answer or a raw data pull.

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When Standard Analysis Is Used

Standard Analysis handles specific questions — questions that can be answered with a single SQL query against your data.

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Examples

These questions share key characteristics:
  • Specific metrics (revenue, orders, customers, ROAS)
  • Clear time bounds (last week, this month, past 30 days)
  • Focused scope (one dimension or ranking)
If you want raw rows or a CSV export rather than an analytical summary, see Raw Data Pull.

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How It Works

Standard Analysis follows a four-step pipeline:
1

Identify Tables

The AI determines which BigQuery tables contain the data you need. For an orders question, it routes to obt_orders; for campaign metrics, it uses rpt_ad_performance_daily.
2

Generate SQL

Using your question and the relevant table schemas, the AI writes a SQL query. This includes appropriate filters, aggregations, and ordering.
3

Execute Query

The query runs against your BigQuery warehouse. Results are validated and any data quality issues are flagged.
4

Generate Response

The AI creates a natural language summary, determines if a chart would be helpful, and packages everything into a Slack response. For raw data pulls, it returns a short confirmation and CSV instead of interpretive findings.

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What You’ll See

During Standard Analysis, the AI shows progress through each phase:

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Response Components

A Standard Analysis response includes:

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Tips for Best Results

“Last 30 days” is clearer than “recently.” The AI handles relative dates well: “yesterday,” “last week,” “past quarter,” “YTD.”
“Revenue” is clearer than “sales.” “Orders” is clearer than “transactions.” Use terminology from your dashboards.
“Top 10 products by revenue” gives a focused answer. “Best products” is ambiguous and may trigger Deep Analysis.
“What was revenue and how did AOV change?” works better as two separate questions. Keep each query focused.
For exports, include the columns you need: “Pull order ID, order date, customer email, discount code, and net revenue for last week.”

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When to Use Standard vs. Deep Analysis

If you phrase a question specifically, it stays in Standard Analysis. “What was our Meta ROAS last month?” is faster than “How is Meta performing?”

Raw Data Pull

How to get raw rows and CSV exports without interpretation.

Deep Analysis

How open-ended questions trigger multi-perspective analysis.

Knowledge Retrieval

How definition and schema questions are handled.

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Learn More About Your Data

Table Schemas

See what columns are available in each table.

Metrics Reference

Understand how each metric is calculated.