A migration you can check, line by line.

Two weeks. Read access. A number at the end.

• A scanner walks the full estate: every Domo dataflow or Alteryx workflow, every dataset, connector, macro, and scheduled job.
• Each one is classified by migration complexity, from standard data preparation that translates cleanly to SQL, to the dense formula logic that needs closer attention.
• Dead and duplicate work is flagged for retirement rather than migration. On a mature estate that is often a quarter or more of the total.
• You get a fixed price and a fixed timeline for the exit, before you commit to anything past this step.

The original is the answer key, not a suggestion.

• Deterministic transpiling handles the structural majority of the estate: joins, filters, aggregations, and standard formula tools.
• AI-assisted conversion handles the bespoke logic. An engineer reviews and signs off on every output, because verifying correctness has to be someone's responsibility, not a model's confidence score.
• The result is SQL, dbt models, and Airflow DAGs running on your own warehouse, integrated with the same sources the originals depended on.
• Everything lands in version control with full lineage, readable by any engineer on your team.

We hand you the diff report, not our word for it.

• Every migrated pipeline runs side by side with its original.
• The harness checks row-level and aggregate equivalence and produces a diff report for each pipeline.
• Nothing is promoted to production until that report comes back clean.
• Your team signs off on the migration using your own numbers.

Your logic, on infrastructure you control.

• Pipelines move over once parity is signed off, on a schedule you set.
• Consumption pricing and proprietary format are gone. The transformation layer reads from tables you own outright.
• Whatever happens next to any vendor you depend on, that part of the business no longer depends on a single company's roadmap or balance sheet.