Services · Quality
Quality assurance generates data in large volumes — and is still often without an answer when an audit asks what a release was actually based on.
Typical situation
„In an audit we cannot show what a decision was actually based on."
Test data is captured but only used superficially. Systematic relationships across lots, lines or time periods stay undiscovered — until a complaint makes them visible.
There are results, but no reliable path from requirement through test to release. In audits and complaints, things get reconstructed rather than looked up.
Services
Systematic analysis across lots, lines and time periods: trends, outliers, correlations. Large data volumes are distilled down to the few statements a decision can actually rest on.
Where are the blind spots? Which tests contribute nothing, which are missing — and which effectively measure the same thing? Known correlations between tests can be used deliberately instead of widening scope across the board.
A continuous path from test requirement through test execution to release — something you can look up at any time rather than reconstruct after the fact.
When analyses are produced with AI support, it must remain provable who reviewed what. That is exactly what KEEL is built for.
Recurring evaluations and reports emerge from existing data — effort shifts from collecting to judging.
Support with root cause analysis, whether the finding comes from a customer or surfaces internally during characterisation and qualification: which data, tests or analyses would have shown the problem earlier — and what follows from that for the test strategy?
A short message is enough. No obligation — by phone just as well.
