AI for data analysis: ask in plain words, get the answer and the query
Ask which customers stopped buying or which region lost margin. The team writes the SQL, runs it and explains the result.
The data exists, but nobody has time to ask it anything.
Sales is in the CRM, orders are in the ERP, and both land in a warehouse that only one analyst knows how to query. Managers wait days for a simple number, or decide without it.
- Managers who need answers from data without writing SQL
- Small data teams with a long queue of ad-hoc requests
- Companies with BigQuery, Snowflake or PostgreSQL and few people who can query them
The agents
Turns a business question into SQL on BigQuery, Snowflake or PostgreSQL, runs it with read-only access and explains the result in a few sentences.
Checks that the answer makes sense against revenue, margin and finance definitions, and points out when a number does not add up.
Turns questions people ask every week into saved queries and dashboards in Power BI or Metabase, so they stop being requests.
How it works
- A manager asks a question in Slack, Teams or the app, in plain words.
- Nina finds the right tables, writes the SQL and runs it with read-only access.
- Darwin checks the result against known totals and business definitions.
- You get the answer, a chart and the query, and can ask a follow-up.
- Questions that repeat become a Metabase or Power BI dashboard once a person approves the definition.
Example of what you get
Answer to a business question
- The answer in two or three sentences
- A chart or table with the numbers behind it
- The SQL used, so an analyst can check or reuse it
- Caveats: missing data, definitions used, what was excluded
What changes
- Managers get answers the same day they ask
- Your analysts spend time on deeper work instead of one-off pulls
- Every number can be traced back to its query
Where a person is still needed. The team reads data; it does not change or delete records in your database. Answers depend on data quality and on clear definitions, which a person should confirm the first time.
Works with what you already use
These are examples. Connect the tool your company already uses for the same job, or your own system through MCP or API.
Data analysis team
Can an AI agent for data analysis be trusted with numbers?
It shows the SQL and the source tables for every answer, and checks results against totals you know. You do not have to trust it blindly: you can read how it got there.
Does a data analysis AI agent need write access to our database?
No. It works with a read-only user. Creating dashboards in Metabase or Power BI is a separate step that a person approves.
How is this different from other AI for business intelligence tools?
It works with the warehouse and BI tools you already have. AI agents for data analysis here also answer in Slack or Teams, check each other's work, and turn repeat questions into dashboards.
How long does it take to connect our database?
Create a read-only database user, connect it, and describe your main tables and metrics. You can ask your first questions in the same session. Opening the team is free; queries after that use credits, shown per answer.
Which databases and BI tools does the AI data analyst connect to?
Nina queries BigQuery, Snowflake and PostgreSQL with read-only access. Bolt builds saved queries and dashboards in Power BI or Metabase. Questions and answers come through Slack, Teams or the app.
Can I ask questions in plain language without knowing SQL?
Yes. Ask as you would ask an analyst, for example which region grew most last quarter. Nina writes and runs the SQL, and you get the answer, a chart and the query. You can then ask a follow-up in the same thread.
What happens when the question is ambiguous or the data does not exist?
The agent asks what you mean before running, for example which revenue definition to use. If the data is not in the warehouse, it says so and lists what is missing instead of guessing a number.
How does it know our definitions of revenue, margin or active customer?
You describe your main tables and metrics when you connect, and a person confirms each definition the first time it is used. Darwin then checks answers against those definitions and known totals, and points out numbers that do not add up.
Can AI for data analysis build dashboards?
Yes. When the same question keeps coming back, Bolt turns it into a saved query and a Metabase or Power BI dashboard, after a person approves the definition. That way recurring numbers stop being requests.
Does it replace our data analyst?
No. It takes the quick, repeated questions off the analyst's plate and shows its SQL so the analyst can review it. The analyst keeps ownership of definitions, data quality and the deeper analysis.