Buster is an AI platform for self-serve analytics, purpose-built for dbt.
How it works
Spin up true self-serve analytics, with a single command
Document your dbt project with AI and empower business users to explore data on their own.
Step 1: Index & document
Index & document your dbt project with `buster init`
1
Connect your dbt repo
Connect your dbt repo, data warehouse, BI tools, etc to Buster.
2
Run `buster init`
Run `buster init` from your IDE or terminal. Buster will introspect your dbt repo in the background; taking a few minutes to analyze metadata, run exploratory queries, and gain a deeper understanding of your data models.
3
Review PR
Buster will write robust documentation files (.yml and .md files within your repo) and send you a pull request to review/merge changes.
Step 2: Give everyone an AI data analyst
Enable business users to explore data on their own
1
Augment your data team with an AI data analyst
With a deep understanding of your data models, Buster can handle data requests for you. Business users can ask Buster questions in plain english and get back detailed reports in 1-2 minutes.
2
Real analysis, not just text-to-sql
Buster is an intelligent "Deep Research" agent. For each request, Buster will:
thoroughly explore relevant docs, models, metrics, etc
run lightweight queries to inform and validate assumptions
adapt as it gets new information
return robust charts, dashboards, or notebook-style reports
Step 3: AI-powered feedback loop
Improve your dbt project over time with background agents
1
Receive Slack alerts when documentation is lacking or unclear
Receive Slack alerts whenever Buster lacks the required documentation to answer a user's data request.
2
Respond & clarify
Use `@Buster` directly in slack to clarify and kick off background documentation agents.
3
Review PR
Background agents will document your clarifications, make model improvements, and send you a PR for review.
AI for data modeling
Build & document your data models with an AI modeling agent
Buster brings AI agents right to your terminal. It's like Claude Code, but purpose-built for data modeling and data cataloging tasks. Turn hours-long workflows into a single command.
Document your data repository
Rapidly document data models. Generate bulk updates whenever changes are made in your repo.
Create new models
Create new models on the fly. Push up new models and have users running queries in minutes.
Improve your models over time
Make changes and optimize existing models over time.
Customers
Top companies use Buster to increase data team productivity
Join top companies who leverage Buster to enhance their dbt workflows & get more from their data.
"A lot of data engineers think self serve is a myth. This is actually self serve, for real for real."
Alex Ahlstrom
Director of Data Analytics @ Angel Studios
"Buster frees me up from the ad-hoc tasks I always had to do, and let's me focus on longer term goals."
Landen Bailey
Data Engineer @ Redo
Enterprise-grade security
Buster is built with enterprise-grade security practices. This includes state-of-the-art encryption, safe and reliable infrastructure partners, and independently verified security controls.
SOC 2 compliance
Buster has undergone a Service Organization Controls audit (SOC 2 Type II).
HIPAA compliance
Privacy & security measures to ensure that PHI is appropriately safeguarded.
Permissions & governance
Provision users, enforce granular permissions, & implement robust governance with ease.
IP protection policy
Neither Buster nor our model partners train models on customer data.
Code-based & git-native
Buster is code based and operates within your own repo.
Open-source
Buster is completely open-source. It's free to spin up locally.
Watch a demo
A new framework for self-serve, purpose-built for dbt
Turn your dbt project into an AI data analyst. Empower everyone at your company to analyze & visualize data on their own.
Start using AI agents in your analytics workflows today