AI & ML Testing
AI systems behave differently to traditional software — and they need a fundamentally different approach to quality. We cover both.
LLM + ML
Full AI Coverage
OWASP LLM
Security Framework
Bias & Drift
Detection Built-in
CI/CD
Pipeline Ready
From large language models to classical ML pipelines, ProgmaticLabs delivers end-to-end AI and ML testing. We validate LLM output quality, detect hallucinations, test for prompt injection, evaluate RAG pipelines and AI agents — and for ML systems, we cover model performance, data pipeline integrity, fairness, explainability, drift detection, and MLOps validation. One partner for the full AI quality spectrum.
What We Cover
End-to-end coverage across every dimension of ai & ml testing.
LLM Output Quality & Hallucination Detection
Evaluate large language model outputs for accuracy, coherence, relevance, and consistency — and systematically test for hallucinations, fabricated references, and confidently wrong answers that are invisible to traditional testing.
Prompt Injection & LLM Security Testing
Test against the OWASP LLM Top 10 — prompt injection, insecure output handling, sensitive data exposure, jailbreaking, and model manipulation — to secure your AI-powered product against adversarial misuse.
RAG Pipeline & AI Agent Testing
Validate Retrieval-Augmented Generation pipelines for retrieval accuracy, context relevance, and response faithfulness — and test autonomous AI agents for decision-making reliability, tool-use correctness, and failure recovery.
Data Pipeline & MLOps Validation
Validate data ingestion, transformation, feature engineering, and storage pipelines for correctness and schema consistency — and test end-to-end ML pipelines including training, versioning, deployment, and rollback for reproducibility.
ML Model Performance Validation
Evaluate ML models against accuracy, precision, recall, F1, AUC-ROC, and business-specific KPIs across diverse test datasets — including regression testing to detect model degradation between versions.
Bias, Fairness & Explainability
Detect and measure bias across demographic groups and protected attributes — and validate model explainability using SHAP, LIME, and attention visualisation, critical for regulated industries and audit requirements.
Model Drift Detection & Monitoring
Continuously monitor for data drift, concept drift, and prediction drift in production — with automated alerting when model behaviour degrades and dashboards giving full visibility into model health over time.
Adversarial & Robustness Testing
Test model resilience against adversarial inputs, noisy data, distribution shift, and edge cases designed to fool or destabilise predictions — for both classical ML models and generative AI systems.
Why AI Systems Need a Specialist Testing Practice
Traditional software either works or it doesn't. AI systems are different — a model can be technically correct but factually wrong, statistically accurate but systematically biased, and operationally healthy today while silently drifting toward failure tomorrow. A language model that hallucinates critical information, a recommendation engine that subtly discriminates, or a fraud model that flags legitimate transactions — these failures don't look like errors. They look like plausible, confident responses that are simply wrong or unsafe.
This makes AI testing fundamentally different from conventional QA. It requires probabilistic evaluation, red-teaming, fairness analysis, adversarial testing, and specialised tooling that most QA teams have never worked with before.
Regulatory pressure is also intensifying. The EU AI Act, NIST AI RMF, and sector-specific frameworks now require documented evidence of bias testing, explainability, and robustness. ProgmaticLabs bridges the gap between AI capability and AI quality — giving your team the confidence to ship AI-powered products that are not just functional, but safe, fair, and reliable at scale.
AI System Audit & Risk Mapping
We audit your AI system's architecture, training data, use cases, and risk surface — identifying the highest-priority testing areas based on impact, user exposure, and regulatory requirements.
Evaluation Framework & Test Dataset Design
We design a bespoke evaluation framework and build curated test datasets covering nominal cases, adversarial examples, edge cases, and fairness distributions specific to your model and domain.
Test Execution & Red-Teaming
We execute structured evaluation suites and adversarial red-team scenarios — documenting failure modes, hallucinations, bias patterns, drift signals, and security vulnerabilities systematically.
Pipeline Integration & Continuous Monitoring
We integrate automated quality gates into your CI/CD and ML pipeline, configure production monitoring dashboards, and deliver detailed reports with risk ratings and remediation guidance.
Tools & Technologies
Industry-leading tools we work with every day.
Ready to elevate your ai & ml testing?
Get a free, no-obligation audit from our specialists and discover where your biggest quality gains are hiding.