Surevisible
Chat

Answers that show their working

Ask which brand lost the most organic traffic last month. Get the answer, the chart, and the exact query that produced both.

The problem

An analytics chat that hands you a confident paragraph and no way to check it is worse than no chat at all. You cannot ship a number you cannot reproduce, so you end up rebuilding the query by hand anyway.

What we mean

The model picks the question; the semantic layer answers it. Every response carries the query it ran: measures, dimensions, filters, window. You can read it, change it, and save it as a chart.

0

answers without the query that produced them

How it works

Start to answer

  1. 01

    Ask

    Plain English, with the brand and the period however you would say them out loud.

  2. 02

    It picks a query

    From the schema only. It cannot invent a metric that does not exist.

  3. 03

    You see both

    The answer, the chart, and the query underneath it.

  4. 04

    Keep it

    Pin it to a dashboard or export the whole thread as a report.

Before and after

What actually changes

The follow-up question

Before

Answering 'and what about mobile?' means rebuilding the report with one more filter.

After

You ask it. The chart updates and the query shows the filter.

Handing the number on

Before

A screenshot in Slack, and a week later nobody can say what date range it covered.

After

An exported report with the window and the query stated on the page.

Auditable by default

The query is not hidden behind a toggle.

  • Measures, dimensions, filters and window, shown
  • Editable in place
  • Saveable as a chart you own

Grounded in your data

It answers from the warehouse, never from memory.

  • No answer without a query behind it
  • Says when data is missing rather than smoothing over it
  • Scoped to the brands in your workspace

Made to be kept

A thread is a document, not a scrollback.

  • Every chat is saved and searchable
  • Charts pin straight to dashboards
  • Word and PDF export

Where the model is not allowed to go

It does not write SQL against the warehouse directly, and it does not decide what a metric means. It selects from declared measures and dimensions. That constraint is the reason the answers can be trusted, and the reason a wrong answer is a wrong question rather than a wrong number.

In short

The question you would have spent an afternoon on becomes a sentence, and the answer arrives with its receipts attached.

See the whole brand

Search, analytics, ads, revenue, CRM and support, pulled into one view per brand and kept current. Connect the first source in a few minutes.