BUSTER, AI AGENTS FOR
DATA ENGINEERING TASKS
For 2 years, we’ve been helping data teams automate repetitive data engineering tasks with AI agents.
This includes things like:
pr reviews
data quality monitoring
detecting & fixing schema changes
generating & maintaining documentation
and much more
If you’d like to be part of our journey + try some of the cool things we’re building, we’d love to chat.
"Buster saves our data team hundreds of hours every month."
Jonathon Northrup
Analytics Engineer, Angel Studios
"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
"Buster helps us keep our dbt project clean, documented, and up-to-date."
Jen Eutsler
Data Engineer, SchoolAI
"Buster's understanding of our dbt project has blown me away. It really gets how our data models fit together."
Cale Anderson
Data Engineer, Remi
What is Buster?
Buster is an AI data engineer that automates data engineering workflows.
Buster can execute complex end-to-end tasks (like documentation updates, pr reviews, schema updates, data quality monitoring) without human intervention.
Buster runs automatically in response to triggers (pull requests, schedules, or custom events like a schema change).
Monitor data quality
└
Run scheduled audits for freshness, null rates, anomalies, referential integrity, etc → send intelligent alerts to your Slack channel.
Respond to failed tests
└
Trigger when a test or build fails → investigate why, open a PR with suggested fixes → send a summary to Slack with a link to the PR.
Review PRs
└
Trigger when a PR is opened → review your changes, run data diffs, flag any breaking changes, etc.
Detect & fix schema changes
└
Detect upstream schema changes → add, remove, or rename staging columns to stay consistent with other models. Fix downstream marts, update tests, etc.
Fix stale tests
└
Trigger when a PR is opened → review your changes, identify stale or missing tests, fix/add tests, commit fixes to the PR.
Optimize & clean up models
└
Run scheduled audits to find unused models and over-materialized tables → open a PR with optimizations.
Auto-update dbt docs
└
Trigger when a PR is opened → review your changes and update documentation to stay in sync with your models.
How it works
1.
Connect your data stack (e.g. dbt project), and Buster will automatically document every model.
2.
Pick a template agent
Select from our library of proven agents and add whatever custom instructions you'd like.
3.
Set a trigger
Define when the agent should run. Agents can run on pull requests, on a schedule, or on specific events.
4.
Let AI do the work
Agents will run automatically when triggered - commenting on PRs, sending Slack alerts, etc.
We are constantly adding agent templates
If you spend time repetitively doing something we haven't seen before, we'll build a custom agent template for it and add it to the library in 2-4 days.
Buster typically takes 10 minutes to set up. If you’d like to take it for a spin, we'd love to chat!
