SchemaStruct started with a simple frustration: warehouse design work was split across SQL files, screenshots, docs, and review threads. We built one workflow for modeling, reviewing, and handing off schema changes without losing context.
Better tooling helps teams review structural decisions earlier and ship cleaner warehouse changes later.
Warehouse models sit at the center of analytics delivery. They deserve tooling that keeps review, implementation, and docs in sync.
Teams jump between SQL files, warehouse metadata, screenshots, and docs. That creates drift, slows approvals, and makes even simple warehouse changes harder than they need to be.
SchemaStruct keeps the model editable as code and understandable as a diagram so teams can move from rough warehouse ideas to approved handoff artifacts without losing context.
These principles shape the editor, the AI workflows, and the way we think about collaboration.
Database design should feel readable and iterative. We focus on flows that make models easier to understand, review, and evolve.
Schemas are easier to trust when diagrams and source stay in sync. We design every workflow around that shared source of truth.
From first draft to production export, the product is built to keep momentum high and friction low for technical teams.
Model ideas before they become migrations.
Keep diagrams useful, not decorative.
Make collaboration feel native to schema work.
Treat AI as an accelerator, not a replacement for judgment.
Start in the browser, shape the model, and hand off approved warehouse changes with cleaner SQL, dbt, and documentation.