Rebuild our revenue model from dbt, stored SQL, and spreadsheet overrides. Keep the outputs consistent before we switch.
Mage
I mapped the logic and prepared a modernized workflow for review.
Use AI to understand existing logic, rebuild it as governed workflows, and validate every output before your team switches over.
Mage turns hidden rules and brittle models into workflows with visible sources, lineage, checks, and release controls.
Understand what exists. Surface the tables, joins, dbt models, SQL, scripts, spreadsheets, and downstream dependencies behind the model.
SELECT2 account_id,3 SUM(amount) AS revenue4FROM stripe_transactions5GROUP BY account_id6@transform8def build_revenue_model(df):9 return validate_schema(df)Rebuild with AI. Generate SQL, dbt, Python, and medallion-style workflow logic from existing rules and business intent.
- WHERE revenue > 018+ WHERE revenue >= 10019GROUP BY account_idGovern every change. Control edits, approvals, deployments, ownership, versions, permissions, and audit trails.
Prove the output matches. Compare old and new results across history, metrics, row counts, schemas, and reconciliation checks.
Mage turns trapped logic into sources, transformations, checks, approvals, schedules, and reusable outputs.
Rebuild our revenue model from the existing dbt project, SQL, and spreadsheet overrides. Preserve monthly revenue, ARR, and account-level outputs, then compare the old and new models before we switch.
The modernized revenue workflow is ready to review. I rebuilt the existing logic as inspectable workflow blocks and compared its outputs against the current model.
99.8% matched · Monthly revenue, ARR, and accounts checked
Ask. Modernize our revenue model without changing trusted outputs.
Discover. AI Sidekick maps sources, joins, SQL, dbt models, scripts, and spreadsheet overrides.
Rebuild. Mage generates inspectable workflow blocks in SQL, dbt, Python, or R.
Validate. Autopilot compares old and new outputs against history, checks, and business rules.
Govern. Teams review lineage, owners, permissions, schedules, and release controls.
Release. The workflow runs in production and becomes reusable context for downstream teams and agents.
Logic discovery Inspect sources, joins, columns, dbt models, SQL, scripts, spreadsheets, and output patterns.
AI-generated models Generate SQL, dbt, Python, R, tests, documentation, and medallion-style layers from existing rules.
Context-aware refactoring Update names, schemas, dependencies, macros, and block logic without losing workflow context.
Side-by-side validation Compare old and new outputs across row counts, schemas, metrics, joins, windows, and reconciliation rules.
Regression and intent checks Describe what must stay true. Mage checks data quality, business rules, and model intent after release.
Production recovery Retry, replay, backfill, recover failed runs, and optimize resources as the model scales.
Reusable business context Preserve definitions, lineage, ownership, checks, run history, and model outputs.
Governed reuse Give teams and agents the right context, permissions, freshness, and scope.
Context activation Use modernized outputs across dashboards, APIs, automations, RAG pipelines, products, and agents.
Bring Mage the model, rule, script, or workflow that still works but is too fragile to evolve.
Move dbt models into workflows that run with sources, checks, schedules, lineage, and downstream delivery.
Turn database-resident logic into inspectable workflows your team can review, test, validate, and change.
Move formulas, overrides, mappings, and metric definitions into governed workflows for revenue, churn, margin, and pipeline.
Start with a dbt project, stored procedure, spreadsheet model, report logic, fragile script, or metric definition your team depends on but cannot safely change.
