Service
AI Safety, Red Teaming & Model Evaluation
Before your AI system talks to customers, supports employees, or influences important decisions, it should be tested by people who know how to break it.
Request an AI Safety AssessmentAI Safety & Evaluation
An untested LLM in production creates real operational and reputational risk. Models can hallucinate, leak sensitive context, behave unpredictably under adversarial prompts, and show bias that basic accuracy scores fail to catch. Katalyst AI Lab runs structured safety and evaluation programs that show where the system breaks, how severe the risks are, and what should be fixed before launch. We also help monitor those risks over time as the model changes.
Definition
What Is AI Red Teaming?
AI red teaming is the practice of deliberately testing an AI system to make it fail in harmful, inaccurate, or unintended ways. Testers use adversarial prompts, edge cases, and jailbreak attempts to expose weaknesses before real users encounter them. In simple terms, it is the AI equivalent of penetration testing in cybersecurity.
Red teaming is not just an automated benchmark. It is carried out by human specialists who think like attackers, understand context, and know how systems fail in practice. The output is a structured report that ranks issues by severity and includes clear remediation steps.
Services
What We Deliver
AI Red Teaming
Description
Structured adversarial testing by human red teamers covering jailbreaks, prompt injection, role-play exploits, indirect instruction overrides, and context manipulation attacks.
OutputFindings report: vulnerability list, severity ratings, attack transcripts, remediation steps
LLM Evaluation Harness Build
Description
Custom automated test suites that measure accuracy, hallucination rate, instruction-following, refusal behaviour, and task-specific benchmarks, integrated into your CI/CD pipeline.
OutputTest suite with code and datasets, baseline benchmark report, regression alerts
Bias & Fairness Audit
Description
Statistical analysis of model outputs across demographic subgroups and sensitive attributes. Built for auditability in regulatory and procurement contexts.
OutputFairness report, disparate impact metrics, failure-mode breakdown, mitigation options
Hallucination Testing
Description
Measurement of factual error rates on your domain knowledge base, with analysis of the topics and query patterns most likely to trigger failures.
OutputHallucination rate per topic cluster + high-risk prompt patterns + retrieval fix recommendations
Guardrail Design & Implementation
Description
Input and output filtering layers, Constitutional AI-style constraint systems, and topic classifiers that reduce harmful or out-of-scope responses in production.
OutputDeployed guardrail layer + test coverage report + maintenance playbook
Alignment Evaluation
Description
Assessment of how consistently model outputs reflect your organisation's values, communication standards, and regulatory obligations.
OutputAlignment scorecard + specific failure mode examples + policy recommendation
FAQ
Questions
What is an LLM hallucination?
A hallucination happens when a language model produces text that sounds credible but is factually wrong, unsupported by the source context, or internally inconsistent. In enterprise settings, that can lead to bad advice, compliance issues, or reputational damage. Measuring and reducing hallucination rates should happen before deployment.
Do you provide a written report we can share with our board or regulators?
Yes. Every evaluation and red teaming engagement ends with a structured report covering methodology, findings by risk category, severity ratings, and remediation recommendations. The format is suitable for internal risk reviews, due diligence processes, and regulatory documentation.
How often should AI systems be red-teamed?
At a minimum, before the first production release and after any major model update or change in use case. Customer-facing systems in regulated sectors usually need this done on an ongoing basis. We offer recurring retainer arrangements for that.
How Confident Are You in Your Model?
We'll design a targeted red-teaming and evaluation programme for your specific AI system and risk profile.
Request an AI Safety Assessment