Does it run queries against production?

It reads what you connect, with the permissions of whoever connected it, and shows the query it ran. Anything that writes or costs real money waits for an approval.

How does it know which table is the right one?

From your models, your team's past queries, and the channels where definitions get argued out. It cites the table and the query, so a wrong assumption is visible rather than buried in a number.

Does this replace an analytics engineer?

No. It absorbs the queue of repeat questions that never should have reached a person, and shows its work so the team can correct it when it's wrong.

AI coworker for data teams in Slack

What changes

Nine hours a week off the ad-hoc queue

The queue of “can you pull…” is the tax that keeps analysts off the questions they were hired for. It is also the easiest thing here to measure, because every request and every answer is already in a channel.

9 hrs

a week back per analyst

Repeat number requests get answered in the channel with the query attached, so an analyst reviews instead of writing it again.

4 min

median answer to a number request, from two days

Asked in the channel, answered in the channel, with the source table and the exact query shown.

Same day

notice when a pipeline breaks

Freshness and row counts are checked every morning, instead of a stakeholder finding a flat chart on Thursday.

Check it yourself. Your warehouse logs every query and your orchestrator every run. Request-to-answer time is in the channel itself, timestamp to timestamp. These are the targets we hold Gini to, not an average across customers.

The first week

Three jobs to hand over first

Pick the one you repeat every week. Gini does it end to end, shows its work in the channel, then offers to keep running it on a cadence.

As they land

Answer the ad-hoc queue

Takes the “can you pull…” questions in the channel and answers with the number, the table, and the query, for a person to check.

Daily, 6:00

Watch freshness

Checks that every pipeline landed and the row counts look right, and says so in the channel before a dashboard lies.

When numbers disagree

Settle the two-numbers argument

When two reports disagree, traces both definitions and writes down which one to use and why.

It reaches the tools data teams already run on

Scoped per person · revocable in one click

Snowflake

Warehouse

dbt

Models

Fivetran

Ingestion

Airflow

Orchestration

Looker

PostHog

Product analytics

GitHub

Analytics code

Notion

Definitions

Plus 200+ more integrations. Gini only reaches what you connect, with the permissions of the person who connected it.

The difference

A chatbot writes SQL.
Gini knows your schema.

Generic SQL is easy. The hard part is which table is the real one, which column was renamed in March, and which of your four revenue definitions this person meant.