AI Test Automation
·VALIDATION DETAILS

Test AI before it creates risk.

We test ML models, LLMs and data pipelines automatically for quality, security, drift, bias and verifiability, before go-live and in operation.

ONE TEST FRAMEWORK ML Models LLMs Data Foundation

ML Models

Performance, drift, and robustness as a continuous process, not a one-off acceptance test.

  • Performance & generalization
  • Stability
  • Drift detection
  • Overfitting / Underfitting
  • Robustness
  • Versioning
  • Documentation

LLMs

Systematically secure against hallucinations, prompt injection, output inconsistency, and data exposure.

  • Prompt tests
  • Hallucination checks
  • Prompt-Injection
  • Data exposure
  • Access control
  • Output guardrails
  • Logging

Data Foundation

Completeness, bias, and distribution shifts as the basis for any reliable model output.

  • Completeness
  • Outliers & inconsistencies
  • Label quality
  • Distribution shifts
  • Bias risks
  • Data drift
  • Versioning

Distinction from AI Services. AI Services develops and integrates AI solutions. AI Test Automation validates, monitors, and documents their behavior. The two complement each other. First AI is built with control. Then it’s made measurable and testable.

Explore AI Services →
·SERVICE BUILDING BLOCKS

What AI Test Automation concretely checks.

Six areas where classic software tests aren’t enough, and how we make them measurable.

Data quality

Completeness, consistency, outliers, and faulty labels.

Model Drift

Gradual changes in inputs and model performance in production.

Robustness

Behavior with unusual or slightly modified inputs.

Bias & Fairness

Systematic bias in data and model decisions.

LLM Security

Prompt injection, data leakage, and disallowed output patterns.

Reproducibility & Audit

Comparable model and data states, audit-proof test evidence.

·METHODOLOGY

From data foundation to production.

Structured approach, from risk classification to continuous monitoring in production.

01Scope & Risk

Use case, model type, data sources, risk class, test goals.

02Test design

Test cases, metrics, thresholds, adversarial scenarios.

03Validation

Run ML, LLM, data, and pipeline tests automatically.

04Monitoring

Drift, output behavior, performance, and anomalies in production.

05Reporting

Technical results, management summary, and audit evidence.

06Improvement

Feed findings back into data, prompts, guardrails, or architecture.

·QUALITY STANDARD

What good AI validation must deliver.

Five criteria for AI tests that hold up in practice.

01

Measurable

Model behavior assessed through defined metrics and test sets.

02

Reproducible

Data states, prompts, and model versions documented comparably.

03

Robust

Tested even under modified, unusual, or critical inputs.

04

Secure

LLM risks such as prompt injection and data exposure tested.

05

Provable

Connectable to governance, risk, and compliance processes.