Evaluating AI systems with confidence.

AI systems are non-deterministic and produce failure patterns that classical test methods do not catch. We support the build-up of a methodical evaluation concept for AI products: model-independent, oriented around the use case and the risk profile.

Request the evaluation concept→

Why AI needs to be tested differently

Four reasons to rethink the test concept.

Non-determinism

Same input, different outputs. Classical test oracles no longer apply. AI needs assessment along several dimensions, with grades rather than exact target/actual comparisons.

AI-specific failure patterns

Hallucinations, bias, manipulation through inputs. Failure classes that classical software quality does not know in this form. Each one requires its own test methodology.

Model drift over time

What works at release can fail after retraining, data changes or a vendor update. One-off testing turns into continuous evaluation.

Justified release confidence

Stakeholders expect a defendable answer to the question of whether the system can go live. Without structured evaluation that answer remains a feeling.

Failure map

Four failure modes with no equivalent in classical software.

Hallucination

Answers sound convincing but are factually wrong. Particularly pronounced in generative systems without grounding in verified sources.

Systematic bias

Results treat certain groups or cases systematically differently. Often inherited from training data, hard to spot without targeted testing.

Manipulation through inputs

Cleverly crafted inputs override the intended behaviour. Relevant wherever users can address the system directly.

Ageing knowledge

What was true at training time no longer holds months later. Facts, regulations and product data move on, the model stays put.

Building blocks

Six building blocks for a sound AI test concept.

// 01

Evaluation dimensions & goals

What is actually being assessed. Accuracy, groundedness, toxicity, fairness, robustness, latency. The selection follows the use case and risk class, not a generic catalogue.

// 02

Test data strategy

Golden dataset, edge cases, adversarial sets, real-world samples. Which quality at which volume, how the data inventory is maintained and developed.

// 03

Metrics & thresholds

Pick the right metric per dimension, set the release threshold and define what happens below it.

// 04

Fairness & robustness

Systematic testing for bias against groups or contexts. Stress tests with unusual and manipulated inputs, red-team exercises against targeted attacks.

// 05

Drift monitoring & re-evaluation

Continuous observation after release. Data drift and concept drift. Clear triggers for re-evaluation and decisions on retraining.

// 06

Reporting & release criteria

Who takes the go-live decision, on which data basis, with which exclusion criteria. Documentation that holds up in a later review.

Check the evaluation concept for our use case →

Method toolkit

What we work with.

Evaluation dimension catalogue

A pragmatic selection per use case and risk class, not from a textbook.

Golden dataset playbook

Build-up, maintenance and versioning of reference data that holds.

Fairness assessment

A structured check with established metrics, traceably documented.

Red-team prompt library

Adversarial test cases for LLM-based applications, curated for practice.

Drift monitoring concept

Triggers, metrics and reaction paths for operations after release.

Evaluation report

Release decisions documented in a defendable way, including for later review.

Questions

What we are often asked.

Is AI testing the same as AI explainability?

No. Explainability explains how a model arrives at a result. AI testing checks whether the result is usable, correct and safe. Both make sense, with different purposes.

Do we need data scientists on the test team?

Statistical thinking helps on the methodological side. For implementation a well-guided test team with dedicated evaluation owners is usually enough. We support the build-up of that capability.

How does this differ from classical testing?

Classical testing checks functionality against specification. AI testing assesses along several dimensions at once, with grades and thresholds rather than a binary result.

What role does the EU AI Act play here?

Depending on risk class, certain evaluations are mandatory, for example fairness assessment for high-risk systems. We integrate the test concept with your AIMS strategy, see also AI compliance.

We use ChatGPT, LangChain or Llama, does that change your approach?

The methodological layer is model-independent. Specific tooling decisions are taken in the design of the evaluation pipeline, not at the concept level.

AI systems that earn trust.

A methodical test concept, defined evaluation dimensions, sound release criteria.