Especially with AI
Test data processing with precision.
SQACE verifies the results of any data processing and migration: independent, provable and scalable.
For AI-generated or classic data processing.
Whether data pipelines, ETL routes, data applications, data interfaces or regulatory reports.
In production at leading German financial institutions, including LBBW and Commerzbank.
Trust layer for result data
What SQACE does, and where it fits
SQACE sits as an independent verification layer over your data processing. It checks the observable result, not the technology. Open the nodes to see the core functions and where they apply.
Expected-result generation from business rules
Expected-vs-actual comparison for verification and regression
Automated deviation analysis
Data integration
4 use casesData migration
4 use casesServices / data products
4 use casesBusiness applications
4 use casesThe difference
We test the result, not just the data.
DQ monitoring checks whether data looks good. A regression test checks that nothing changed. Both miss the expensive case: processing that runs without error and produces a plausible but wrong result.
SQACE computes the expected result from business rules, as a second, independent calculation, and compares it with the actual. That surfaces logic errors monitoring and regression do not find.
Method
Start small, deepen release by release
No long rollout. You start with fast baseline coverage and build out rule-based verification step by step.
-
Baseline coverage
Regression tests against a verified baseline, runnable on day one, no rule set required.
-
Rule-based verification
For changed fields, rule sets generate the expected result. The comparison shows the rule behind each value.
-
Understand deviations
Automatic classification by pattern, drill-down to field and rule level, handover to your test management.
Evidence
In production, with numbers you can follow
70-80
applications under verification
At LBBW, scaled from an initial 6 applications (public on sqace.io).
15,847
records verified automatically
Example of a capital-gains-tax calculation, checked fully rule-based.
~80%
of data processing coverable
With the rule-based approach. A statement about coverage, not effort.
For your role
One entry point per perspective
CDO / Head of Data
Provable, automated, comprehensible for the business side, with deviations tracked across runs. See use cases
Head of Test / QA
Start now with regression tests, no rule set needed, then expand release by release. Start small
Business / BA
Follow and validate the test logic in the visual query builder, on real data, without SQL. How it works
IT & security
An independent verification instance outside your pipeline stack: on-premise or EU cloud, REST API, audit trail. Security & architecture
Why now
AI writes the pipeline. Who checks the result?
More and more, AI generates processing and its tests in one go. But syntactically correct does not mean functionally correct. What is missing is an instance that checks the result independently of the system that produced it.
That is SQACE: a neutral verification instance that checks the functional result with rule-based expected-vs-actual comparisons, whether the pipeline is hand-written or AI-generated.