1. SNADLY interprets a source system
Generates staging models, intermediate logic, marts, tests, and documentation in standard dbt SQL.
For BI teams
More models into production. Same team. Best practice by default.
Your team knows what good looks like. They are also drowning. SNADLY generates standard dbt models, tests, and docs — raises a PR against your repo — and your engineers decide what ships.
The problem
Your team is so busy building they cannot govern.
The business asks for a dashboard. Your analytics engineer estimates 3 weeks. Meanwhile, marketing has started building their own reports because BI is too slow — numbers now disagree across the company. Tests get skipped to hit deadlines. Documentation is 6 months behind. The warehouse works, but it is held together by one person's memory.
The fastest path to better governance is taking the repeatable build work off your team so they have the bandwidth to review, test, and document at the standard they want.
How it works with your team
Contributes like another team member.
2. A PR lands in your repo
Your analytics engineer reviews the pull request like any other contribution — comments, change requests, approval.
3. Your team merges what passes review
Your governance. Your codebase. Your standards. Nothing ships without your sign-off.
4. You own everything going forward
Modify any model with or without SNADLY. Hand it to a new hire. Move it to a different repo. It is just dbt.
What you get
Real dbt. Not dbt-style.
Staging and marts
Clean, documented models that map directly to your source systems and business entities.
Tests and lineage
Data quality assertions and column-level lineage with every model — whether you have these today or you are trying to get there.
Real dbt in your repo
Standard dbt SQL your engineers can inspect, modify, and extend with or without SNADLY.
Docs that stay current
Column descriptions, assumptions, and transformation logic that ship with the model rather than lagging behind.
No black box
Standard dbt SQL. No AI at runtime. Your engineers are the gate.
Models are generated once — after that, they are just dbt code in your repo. You can modify anything, move it anywhere, hand it to a new hire six months from now. Nothing depends on SNADLY being available.
The AI writes the first draft. Your team reviews and owns the finished model. Governance is not automated away — it is given the room to actually happen.
First step
We raise a PR. Your engineer reviews it.
SNADLY generates staging models, marts, tests, and docs for one of your real source systems. The output lands as a PR against a test repo. Your analytics engineer reviews it like any other contribution. If she would merge it, you have something worth scaling.
Book a PoC