Know what's wrong with your data platform, and what to fix first
A fixed-price review of your dbt project and repository. Every finding names the file, the evidence and the owner.
£2,000, fixed. Most reviews are back within a week.
Example report · analytics
Start with the failing orders test and the Stripe feed that's 19 hours stale. Both reach reports people use.
6 of the 184 findings in this review.
models/marts/schema.yml The test not_null_fct_orders_customer_id is failing on 1,284 rows.
Fix: Fix the data or retire the assertion — a permanently failing test trains the team to ignore the suite.
- We don't need access to your data
- We don't need your credentials
- Your source code doesn't have to leave your network
Most reviews start with one of these.
You've inherited a data platform
Find out what's fragile before it breaks, ranked from the models your dashboards read.
You've been asked to sign off work
A summary for non-specialists, with the evidence underneath for the team.
You're about to put AI on your data
See where it would go wrong first, with a level from 0 to 4 for enablement, use, governance and security.
Three steps, usually within a week.
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Send what you already have
Your dbt
manifest.jsonis enough to start. Add query history and your rates, and we also price each model and dashboard. -
We run the checks, a person reads them
Every model, source and test, not a sample. Each check is a written rule, not an LLM's opinion.
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You get the plan and a walkthrough
A report with the verdict, a worklist for your backlog and a private portal, then we take your team through it.
Four questions. Fifteen areas.
Is it correct?
Metric consistency, metric definitions, build reliability, source freshness
Will it hold up?
Change risk, build performance, orchestration, platform and lineage
Who owns and uses it?
Ownership, documentation, actual usage, governance readiness
What's beyond dbt?
BI semantic layer, AI lifecycle, SQL without dbt
Works with
- dbt Core
- dbt Cloud
- SQLMesh
- Dataform
- Plain SQL
- Looker
- Power BI
- Tableau
- Airflow
- Dagster
- Prefect
- OpenLineage
- Marquez
- Snowflake
- BigQuery
- Databricks
- Amazon Redshift
- Amazon Athena
- GitHub
- GitLab
- Bitbucket
- Terraform
Fixed prices, no sales call.
Technical Review
£2,000
One dbt project and its repository: every finding read by a person, the report, the worklist and a walkthrough. No subscription.
Startup Review
One smaller dbt project and a small team, or none.
Reassessment
After a review and your changes, we confirm what's fixed and flag anything new.
Migration Assurance
We compare the estate before and after a move. How it works.
Got a larger estate? We'll agree the scope and the price with you before any work starts. Every price.
We already run dbt_project_evaluator. What does this add?
Mostly execution and ownership. dbt_project_evaluator checks what the project declares. We also read what the last build did, who each asset routes to, and how the repository is looked after. If your models are already tested, documented and owned, say so when you get in touch and we'll tell you honestly whether a review is worth it.
What can't you see?
We never read a row of your data. From catalog.json we use only the warehouse's own statistics about each relation. We can't see how jobs ran or which dashboards people open unless you send that history. And the fixes are yours: we write the report and the worklist. Everything we read and can't see.
Find out what to fix first.
We'll confirm whether the review fits, and come back with a scope and a price within the week.
Not ready? See a sample report →