Teams validate AI assistants by hand,asking sample questions, reading answers, and jotting notes in spreadsheets. The AI Testing Framework compresses that into a single working day, letting your team run structured, reproducible benchmarks with results traceable to the exact model, data, and configuration used.
Organizations increasingly rely on Retrieval-Augmented Generation systems to help employees answer questions about internal documents, regulations, technical manuals, and policy material. Today, validation is often informal: employees ask example questions, read the answers, and decide subjectively whether the assistant performs well enough.
This creates real problems. Test questions aren’t consistently documented, results can’t easily be compared across model versions, it’s unclear whether the assistant retrieves the right documents, errors may surface only after the system is already in daily use, and evidence for internal approval or governance review is difficult to collect.
The AI Testing Framework helps organizations define, implement, and run reproducible test suites for their AI systems, using the Fraunhofer AI Assessment Catalogue as a reference for quality and trustworthiness requirements.
Requirements such as correct document retrieval, source grounding, and reproducibility are translated into measurable test criteria, then implemented as reusable workflows using structured benchmarks like CoverRAGBench and deployed on compatible container-based infrastructure.
Teams run the suite across model versions, prompt settings, and document collections, and receive documented results that link requirements, tests, configurations, datasets, and model versions, with human-in-the-loop validation throughout.
Validate in a Day, Not a Week
Cut manual validation for each new assistant version from five working days to one by reusing the same structured test suite.
Compare Versions with Confidence
Run consistent benchmarks across model versions, prompts, and datasets to see clearly whether each change improves results.
Trust Traceable Results
Keep every result linked to the exact model version, dataset, prompt, and document collection used.
Reuse Structured Test Suites
Build modular, repeatable workflows that extend to new models, datasets, and application scenarios over time.
Support Governance and Approval
Generate documented, audit-ready evidence to support internal review and governance sign-off before rollout.
