What is Buster?
Buster is an AI agent platform built for analytics engineering. It provides data teams with AI agents that keep their dbt projects reliable, documented, and consistent — automatically.
Keep your dbt project reliable, documented, and consistent
Buster runs AI agents in your CI/CD and on recurring schedules. Agents deeply understand your models, schema, lineage, and metadata - and are triggered whenever your code changes to validate, document, and repair what’s needed.
Run AI agents on a schedule
Recurring agents audit your dbt project for drift, stale tests, and outdated docs, keeping your warehouse clean without manual maintenance.
Schedule an agent
Name
Weekly Data Reliability Check
Frequency
Weekly: Mondays at 2:00 AM
Project scope
analytics/dbt_prod
Custom instructions
Run data quality and schema consistency checks across all production models every Monday.
For any model that fails more than two tests, do the following:
Summarize the root cause (schema drift, null violations, or unexpected value distribution).
Compare the result to the last 7-day window of tests to identify regressions.
If the failure appears new, create a GitHub issue linking to the affected dbt model.
Post a summary in #data-quality with:
number of tests run and failed
affected models
a short risk classification (low, medium, high)
If no issues are found, quietly log “✅ All checks passed” in the run history.
Use Buster from your terminal or IDE
Run agents on demand right from your terminal or IDE for ad-hoc tasks — like building new models, making changes across cascading models, etc.
models/marts/fct_customer_revenue.sql
{{ config(materialized='table') }}
with customer_orders as (
select
customer_id,
sum(order_total) as total_spent,
count(order_id) as order_count,
max(order_date) as last_order_date
from {{ ref('stg_orders') }}
group by 1
),
customer_tiers as (
select
customer_id,
case
when total_spent >= 10000 then 'platinum'
when total_spent >= 5000 then 'gold'
when total_spent >= 1000 then 'silver'
else 'bronze'
end as customer_tier
from customer_orders
),
final as (
select
c.customer_id,
c.customer_tier,
c.total_spent,
c.order_count,
d.region,
d.first_purchase_date
from customer_tiers c
left join {{ ref('dim_customer') }} d using (customer_id)
)
select * from final
Ask Buster to build models, update docs, explore…
Sonnet 4.5
Use cases
Teams that use Buster spend less time on maintenance and ship better data products faster—with higher reliability and cleaner models.
Identify data quality issues
Profile and validate models on every PR to catch anomalies, schema drift, and missing tests before they're merged to production.
Catch breaking changes early
Review PRs in upstream application repositories to flag breaking changes before they cascade into downstream models.
Automate test creation
Use agents to generate new tests on PRs and improve dbt tests on a regular cadence, expanding coverage and preventing silent regressions.
Enforce modeling standards
Apply naming, testing, and structural conventions across your dbt project automatically—no manual policing required.
Audit your warehouse on a schedule
Run agents on a recurring basis to interrogate your dbt project and find stale models, unused tests, outdated docs—keeping your warehouse clean without manual maintenance.
Auto-update and maintain docs
Update YAML and markdown docs with every model or schema change so your project stays accurate and AI-ready.
Documentation & AI context
Index your dbt project and generate robust documentation. Documentation is updated on every PR and can be used as AI context in tools like Omni, Hex, Snowflake Cortex, etc.
Automate documentation, from init to every PR
Run buster init once to generate complete project documentation, then let agents update it automatically on every pull request — keeping your metadata and docs perpetually in sync.
Buster is exceptional at writing context-rich documentation. Below is an actual file that was generated by Buster, without any human intervention or guidance:
Power AI tools and self-serve analytics
Buster’s documentation layer is fully compatible with tools like Hex, Omni, and Snowflake Cortex — and also fuels Buster’s built-in AI reporting and self-serve analytics experience.
Sales Over Recent Months
update me on our sales over recent months
Reasoned for 6 minutes
Sales Over Recent Months
Version 1
I spent a few minutes digging into sale data from recent months and spun up this report for you.
It looks like sales dipped last month, likely as the result of a 28% decrease in marketing spend. This especially impacted sales in the Electronics Category.
Ask a follow up...
Our AI may make mistakes. Check important info.
Report
File
Sales Over Recent Months
Apr 17, 2025
•
Created by Buster
Last month, sales experienced a significant decline - dropping nearly 21% compared to the previous month. This report investigates the reasons behind this decline using historical sales data, marketing spend, and competitor activity.
Sales Decline in Electronics Category
Last month's sales fell nearly 21% below the previous month, with a significant 67.42% drop in the electronics category compared to the previous month.
Monthly Total Sales and Monthly Electronics Sales
Last 6 months
•
What were total sales and electronics sales over the last 6 months?
Total Sales
Electronics Sales
Impact of Reduced Marketing Spend
Marketing spend decreased by 28% last month. Regression analysis indicates a strong historic correlation (R² = 0.78) between your marketing spend and sales, suggesting this reduction significantly contributed to the sales dip.
Marketing Spend & Electronics Sales
Last 6 months
•
What was marketing spend and electronics sales over the last 6 months?
Marketing Spend
Electronics Sales
Customers
4x fewer breaking changes in prod
3x more data quality issues detected
3x faster PR cycles
100% of models documented
16.5x increase in self-served data requests
"Buster frees me up from the ad-hoc tasks I always had to do so I can focus on longer term goals."
Landen Bailey
Senior Data Engineer, Redo
"A lot of data engineers think self serve is a myth. This is actually self serve, for real for real."
Alex Ahlstrom
Director of Analytics, Angel Studios
Enterprise & 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 Type II compliant
Buster has undergone a Service Organization Controls audit (SOC 2 Type II).
HIPAA compliant
Privacy & security measures to ensure that PHI is appropriately safeguarded.
Permissions & governance
Provision users, enforce permissions, & implement robust governance.
IP protection policy
Neither Buster nor our model partners train models on customer data.
Self-hosted deployment
Deploy in your own air-gapped environment.
Secure connections
SSL and pass-through OAuth available.