You outgrew your data stack.
Migrating it is holding you hostage.
We can help.

Tabular record of every item to be migrated — drives waves, sequencing and daily status.

… and the rest of the estate

The client's legacy dbt package, written in the legacy dialect, is mirrored into the delivery workspace.

An audit maps every model's features and dependencies, and batches the estate into waves.

SQL and configuration rewritten to the target dialect under the agreed translation guide — explicit casts on joins, safe division, JSON handling, materialisation rules.

Translated files live in the workspace, not the client repo.

Every translated model must parse and compile in the target dialect, and pass the engagement's lint rules.

Nothing proceeds on translation alone.

Built into a scratch dataset in the sandbox. A cost screen runs first: an estimated-scan check holds unusually expensive builds for authorisation.

Proves the model runs against real data without touching production.

Compares the migrated package's sandbox output with the source warehouse's contents — as much data as the sandbox holds: row counts, window endpoints, sums, distinct counts.

Passing models are grouped onto a branch of the client's repo. Four gates before release:

A defect here pulls the model from the branch.

The client reviews the pull request and their CI runs independently. Merge places the model in the production codebase.

The client's scheduler builds the merged models into production on its normal cadence.

Compares the production model's output in the target warehouse with the same model's output in legacy production. Internal models ship then verify — fixes go forward via a new pull request.

Availability gaps — source history missing on the target — become numbered requests to the client. The model waits; resolution re-runs stages 4–5. Gaps found anywhere in the flow land on this list.