Response quality
Representative cases, context and boundaries. Strahlkraft documents memory, assistant behaviour and model-change tests.
Evaluation and governance
Check that the system responds well and its controls work.
Define what AI should do and how to demonstrate it. Evaluate responses, data and actions; findings become corrections and tests that support change review.
Test failure and its valid counterpart.
Teaching example of evaluation. These illustrate the method; they are not measured client results.
| Control | Must stop | Must allow |
|---|---|---|
| Provenance | Data without required support | Data with a valid reference |
| Access | Operation outside permission | Operation within scope |
| Output | Format violating the contract | Output matching the schema |
Data quality
Explicit sources, dates and confidence. Despegue cross-checks claims and preserves refuted data to detect its reappearance.
Controls put to the test
Test the case that must stop and the valid case that must pass. In industrial work, this distinguishes a described control from one that detects failure.
Decisions and accountability
Document who accepts an output, what changes and what remains pending. Translate data and management requirements into measures for the system and purpose.
Tests and documentation support review of a specific scope. Strahlkraft includes documented AI management that continues to evolve, referencing ISO/IEC 42001.
Explore the work
Different needs. Systems built with business judgement.
Discuss your project
Start with the problem you want to solve.
Tell us what you need to build or improve. Technical work starts with understanding your operations.