Buster helps data teams build & deploy reliable AI data analysts across their org - with robust controls & guardrails.
It's open-source & purpose-built for dbt.
Home
Inbox
My stuff
AI data analyst
Metrics
Dashboards
Collections
Automations
Admin tools
AI data engineer
Logs
Terms & definitions
Datasets
Try
Invite people
Monthly Sales by Sales Team
Show me our monthly sales by sales team
Sure, I’ll create a chart that shows you your monthly sales by sales rep. Lets build your chart:
Monthly Sales by Sales Team
Thought for 13.1 seconds
Can you make it a nice soft purple and pinkish red?
Let me change the colors to be a softer purple/pinkish red color.
Monthly Sales by Sales Team
Thought for 1.1 seconds
The colors on the chart have been changed to a purple/pinkish red color. Let me know if you need me to tweak it some more.
Edit the chart or follow up...
Our AI may make mistakes. Check important info.
Chart
Results
Thought
Monthly Sales by Sales Team
Last 6 months
•
What are our monthly sales, broken down by sales team?
$48,080
Team 1
$56,192
Team 2
6k
5k
4k
3k
2k
1k
0
Jul
Aug
Sep
Oct
Nov
Dec
Home
Inbox
My stuff
AI data analyst
Metrics
Dashboards
Collections
Automations
Admin tools
AI data engineer
Logs
Terms & definitions
Datasets
Try
Invite people
Update Sales Pipeline Dataset
can you push a change to sales_pipeline? i need to add a column that calculates time since previous stage
Sure, I’ll spin up a new column in sales_pipeline that calculates the total time since the previous stage. Lets add that column.
Update Sales Pipeline Dataset
Thought for 14.8 seconds
I was able to add that column to the dataset. It calculates the amount of time (days) an account has spent in their current stage.
Edit the chart or follow up...
Our AI may make mistakes. Check important info.
Sales Pipeline Dataset
Reset
Publish
SQL
Metadata
Open repo
Copy SQL
Run
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Account_ID
Nate Kelley
Blake Rouse
Gregory
Dallin Bentley
Another name
Another name
Another name
Another name
Another name
Another name
Another name
Another name
Another name
Another name
Another name
Another name
Account_Name
9.1
4.3
10
8.9
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
Stage_Name
Raisin bran
Pizza
Quinoa
Meats
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Stage_Start_Date
Raisin bran
Pizza
Quinoa
Meats
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Days_In
Raisin bran
Pizza
Quinoa
Meats
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Favorite food
Raisin bran
Pizza
Quinoa
Meats
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Favorite food
Raisin bran
Pizza
Quinoa
Meats
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Favorite food
Raisin bran
Pizza
Quinoa
Meats
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Various Items
Buster is an AI data analyst, built for your dbt project
We believe that the future of AI analytics is about helping data engineers build powerful, self-serve experiences for their users. Below is an overview of how we help you do this.
Purpose-built for dbt
Buster is purpose-built for dbt
Feeding an LLM a raw warehouse schema will result in a very confused LLM. Or worse, a confident LLM that makes incorrect assumptions.
This is why we specifically built Buster for teams that use dbt.
Buster is intentionally built to operate within the scope of your dbt models. This does a couple of really important things:
It allows you to document all of your business logic & tribal knowledge into code
It creates a single source of truth
It reduces the assumptions that AI agents need to make
It allows us to validate queries & deterministically identify when an AI agent makes assumptions (e.g., does things that are not defined in your models)
It allows you to continuously improve your AI agents & define nuance over time
Code-based & Git-native
Everything in Buster is code-based
Everything in Buster is code-based and lives (as files) in your own Github Repo. This enables you to manage everything from your CLI & CI/CD pipeline.
More importantly, it enables things like:
Version control
Support for multiple environments
Identifying discrepancies or duplicative content
Auto-generating pull requests for suggested model improvements
Generating bulk changes across your repo
Automatically fixing impacted dependencies when there are breaking changes
And much, much more (we're building some cool stuff)
Enrich your dbt models
Enrich your dbt models with AI-generated documentation
With our CLI tool, you can auto-enrich your dbt models with additional metadata & documentation. The CLI tool can do things like:
Scan your source directory for SQL files & existing documentation
Automatically create YAML documentation files
Generate robust documentation with custom metadata fields (e.g., model descriptions, column descriptions, enum indexing, data types, predicted joins, etc)
Document unique business context & terminology
Changes in your dbt repo will automatically be reflected in Buster. You can also push datasets created in Buster to dbt, creating a new pull request in your git repo.
Implement guardrails & AI safety
Implement guardrails & control AI querying capabilities
Buster is trained to strictly work within the bounds of the dbt models that have been granted query access (by you). Anytime Buster attempts to generate a query that is not explicitly defined in the underlying data model, the query gets flagged.
We do this by running a set of rigorous tests every time Buster generates SQL or Python. These evaluations detect lots of things, but the most important are:
Did the AI have to make assumptions about things that are not explicitly defined in the data model(s) or data catalog?
If assumptions were made, how severe was each individual assumption?
You can do all kinds of automated things with flagged queries:
You can allow the AI data analyst to run the query & notify the end user of the assumptions that were made.
You can block the query from being run & return an error message to the end user.
You can send a ticket to Jira or Linear for review.
You can have Buster generate a pull request with a suggested model improvement or documentation update.
Self-improving data models
Improve data models with auto-generated pull requests
Buster was intentionally built to create strong feedback loops between the BI layer (the AI data analyst) and the modeling layer. This allows Buster to constantly be optimizing your dbt models & documentation files.
Lets say a user requests some kind of analysis, but it introduces a new concept that isn’t clearly defined in your data model. Buster will identify this & flag the request. Then, Buster can do some really powerful things:
identify a potential model improvement → create a new branch → generate an update to your data model → send you a pull request
You can then review the request & merge it with one click.
Open source
Open source and deployable anywhere
Buster is fully open source.
Deploy it in your own cloud. No vendor lock-in. No surprises.
Ready to start leveraging AI data agents at your org?