Stop hand-writing SQL and Python. Tell Sprinkle's AI agent what you need — it drafts the transform, infers dependencies, generates tests and ships a tested, scheduled pipeline your team can review.
The agent reads your warehouse schema, drafts the transform in SQL or Python, wires it into the DAG and adds tests — you stay in the loop, reviewing diffs before anything runs in production.
"Monthly revenue by region from paid orders" is enough. The agent knows your schema, joins and grain — no boilerplate, no scaffolding.
The agent writes warehouse-native SQL for analytics work, and switches to Python when you need pandas, ML or anything SQL can't express.
Every generated transform lands as a PR with diff, inferred lineage and auto-generated tests. Hit approve and it's scheduled. Reject and iterate by prompting again.
As the agent generates each transform, it reads upstream references and slots the new node into your dependency graph automatically — no manual wiring, no YAML, no broken refs.
Every generated transform ships with assertions the agent inferred from the schema and the prompt — uniqueness, nullability, referential integrity. You review them in the PR; the pipeline pauses if they fail.