Natural-language revenue analysis

The challenge

Revenue questions arrive faster than the analytics team can answer them, and by the time a number comes back the meeting that needed it has moved on.

The scenario

A finance lead wants to know which customers grew this quarter and why, before a board review on Thursday. The answer lives across a billing database and a warehouse, and nobody in the room has credentials for both.

What goes in

Billing database

Invoices, line items and credits in PostgreSQL.

Warehouse

Modelled revenue and account tables in Snowflake.

What you can ask

Illustrative prompts. Results depend on your own data and are not deterministic.

• “Which customers increased spending by more than 25% this quarter?”Copy
• “Break that growth down by product line and region.”Copy
• “Which of those accounts also raised support tickets?”Copy

How Clearception approaches it

How the work is structured. Each step shows what its output is grounded in.

1. 1

   Resolve the question

   AI-inferred

   Identify which sources hold spend, period and customer identity.

2. 2

   Plan the retrieval

   AI-inferred

   Decide the joins and the period boundaries, asking rather than assuming where the schema is ambiguous.

3. 3

   Execute read-only

   Source

   Run the plan through the gateway under registered row limits.

4. 4

   Assemble the answer

   Observed

   Combine results and attach the query and rows behind each figure.

What you get

The shape of what comes back — not a promised result.

• Ranked account list

  Observed

  Accounts with period-over-period change, each expandable to the invoice rows behind it.

• The query that ran

  Source

  The generated SQL, shown so someone who knows the data can check the joins.

Why it matters

Answers inside the meeting

The question gets resolved while it still matters.

Checkable numbers

A wrong assumption is visible in the query rather than buried in a slide.