Who this is for
Analytics lead or head of operations at a tech company with conflicting numbers, or a failed 'ask the data' pilot.
Two dashboards show different revenue, or a pilot that let staff ask the warehouse questions gave wrong answers.
The result
Up to ten metrics are defined as code (semantic-layer files or tested views) on one schema, and a 30-question set returns figures that reconcile to control totals you sign off. In 5 planted tests, questions outside the definitions return an error or refusal, not a figure. A plain query-writing baseline is reported beside them, not promised.
What is included
- Up to ten metrics on one schema, as semantic-layer files or tested views; an entry version covers three
- A pull request with definitions and tests, and a glossary
- A 30-question set with expected values: 20 for tuning and 10 that your named person writes and keeps from us until the test run
- A benchmark script and a results table including a plain text-to-query baseline
- Work on a read-only clone with personal columns masked
You receive
- The pull request with definitions and tests
- The glossary
- The 30-question set with expected values
- The benchmark script
- The results table with the baseline
What is not included
- Deciding what a metric should mean: stakeholders sign each definition
- Access to your live warehouse or production data
- Dashboards or reports
- Questions across many hops of joins, which a semantic layer may not answer
- Personal data
What we need from you first
- The metrics you want defined, in plain words
- Which tool holds the data (dbt, plain SQL, a warehouse) and the table count
- No real records, personal data or credentials in the first enquiry
Never send passwords, keys, customer records or confidential code in the first enquiry. Secure handover is agreed after scoping.
How we check it is done
- Each defined metric reconciles to the control total you supply, within the tolerance you set in writing.
- All 10 questions your named person kept back until the test run return the expected values through the definitions. A wrong figure on any of them fails the whole test. This is a small test (10 of 10 shows the true rate is at least 74% with 95% confidence) and the report says so.
- 5 out-of-scope questions, which your named person adds and we do not see beforehand, each return an error or refusal, not a figure. One figure returned for any of them fails the whole test.
- A plain baseline (the same questions answered straight from raw tables) is run on the same set; both results are reported, and neither is promised for future questions.
- On a second snapshot with changed inputs, each metric moves by an independently computed amount.
Your named person signs the control totals and the results, and your team reviews and merges the pull request. Payment follows sign-off.
When we would stop or decline
- Stakeholders dispute the definitions: that is a decision, not engineering work
- Only a live production warehouse is available
- Personal columns cannot be masked
- You want the tool to answer anything
Questions
Does this make 'ask the data' work?
It makes answers through defined metrics checkable. In our test, five questions outside the definitions are refused, not guessed; we cannot promise the same for every future question.
Who decides what a metric means?
Your stakeholders. We write down and test what they agree.
Do you need my live warehouse?
No. A read-only clone with personal columns masked is enough.
Price and terms
From £495 · untested offer price. The fixed price is set in the written quote. Nothing is charged before you agree it, and payment follows your sign-off that the control totals reconcile.
This is a new service with no published client results. The price is a starting point we have not yet tested with buyers. Nothing is ordered or charged by the enquiry. The full specification is on the Synthetic Industry catalogue.