Validation of ML models, LLM applications, and data pipelines. Automated against drift, hallucinations, bias, data exposure, and compliance gaps.
ML models, LLMs, and data demand different validation logics. We bring them together into one consistent test flow with shared documentation and evidence.
Performance, drift, and robustness as a continuous process, not a one-off acceptance test.
Systematically secure against hallucinations, prompt injection, output inconsistency, and data exposure.
Completeness, bias, and distribution shifts as the basis for any reliable model output.
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 →Six areas where classic software tests aren’t enough, and how we make them measurable.
Completeness, consistency, outliers, and faulty labels.
Gradual changes in inputs and model performance in production.
Behavior with unusual or slightly modified inputs.
Systematic bias in data and model decisions.
Prompt injection, data leakage, and disallowed output patterns.
Comparable model and data states, audit-proof test evidence.
Structured approach, from risk classification to continuous monitoring in production.
Use case, model type, data sources, risk class, test goals.
Test cases, metrics, thresholds, adversarial scenarios.
Run ML, LLM, data, and pipeline tests automatically.
Drift, output behavior, performance, and anomalies in production.
Technical results, management summary, and audit evidence.
Feed findings back into data, prompts, guardrails, or architecture.
Five criteria for AI tests that hold up in practice.
Model behavior assessed through defined metrics and test sets.
Data states, prompts, and model versions documented comparably.
Tested even under modified, unusual, or critical inputs.
LLM risks such as prompt injection and data exposure tested.
Connectable to governance, risk, and compliance processes.
