Inquisitor Labs
AI behavioural testing and human–AI interaction

AI TESTING: Practical methodologies you can implement today for evaluating AI in workflows and conversations
AI AUDITING: Verifying AI output across domains to expose failures and operational risk
AI STABILITY: Reimagining current systems for consistent, reproducible behaviour
AI SAFETY: Building the foundations that AI governance and legislation haven’t defined

AI fails in deployment because its behaviour isn’t measured under realistic conditions. That’s the gap we specialise in.

What does AI do when real people & real work interact with it? Our practical, evidence‑driven evaluations reveal liability risks that prompt‑based testing never catches. Our work gives organisations a grounded view of how AI behaves in practice and the control needed to manage it.

AI Brand Visibility Audit Service

AI is talking about your brand behind your back.

Customers ask chatbots if you’re safe, legitimate, worth the money, or better than competitors - and AI answers with whatever narrative it has absorbed. ChatGPT alone now answers billions of questions each day. Some of those questions are about your brand. An AI Brand Visibility Audit gives you the missing layer now standing between your brand and the public.
Find out what AI is saying about your brand.

AI & UX Evaluation Service

We evaluate AI systems using real user interactions across real contexts – from everyday work tasks to AI‑driven apps and chatbots. We help identify AI & UX failures, behavioural issues and points of friction early, before they turn into customer impact or regulatory and financial consequences.

EU AI Act – Support, Resources and Guidance

Support, resources and guidance for organisations that must comply with the EU AI Act. Find out whether the Act applies to your systems, understand what evidence is required, and learn how to test AI using real‑world conditions to meet the new legal obligations.

AIOBES – AI Operational Behavioural Evaluation Standard

The emerging standard for AI behavioural safety, reliability, and structured evaluation across modern AI systems.

How to Work Effectively with AI

Let’s face it - AI does not always live up to the hype. It goes off‑track, changes tone, forgets things, and can give different (or inaccurate) answers to the same request. Many people don’t know why it happens or how to make it more manageable. So we’ve developed some lightweight tools and straightforward tips to help you get more out of AI in your everyday work.

Archive of AI Anomalies

Strange and unusual behaviour from the frontiers of latent space. When we push the edges of AI behaviour in our reseach, we often get surprising results. Some have been declassified here as a public resource.

Published Research

Public research linked to Inquisitor Labs. Published on Zendo. Updates will be added as further work is completed.

AI Behavioral Testing Under Realistic Conditions Matters

AI systems need to be evaluated using realistic conditions, behavioural evaluation, and scenario‑based analysis. It is the only way to identify failures, measure reliability, and generate evidence that aligns with modern AI governance and regulatory requirements. Find out more here.

LLM Inquisitor AI Testing Methodology

Convesrational AI Testing

“There is no magic prompt with which you can test AI. What you can do, is give AI real work under realistic conditions and observe what happens.”

LLM Inquisitor is a practical methodology for evaluating and stress-testing AI systems under real-world conditions. It exposes hidden failure modes, behavioural drift, data leakage, hallucinations, and workflow-level risks that traditional prompt testing cannot detect. Structured, scenario-based, and auditable, it gives developers, testers, and governance teams a repeatable way to understand and manage AI behaviour.

Get your copy from Gumroad

“Users do not ask one question and leave. They engage in multi-turn, free-flowing dialogue.”

Vectored Conversational AI Testing is a behavioural methodology built for real conversational interactions, not single-prompt checks. It evaluates how AI systems adapt, drift, recover, escalate, or fail across full conversational arcs. Structured, repeatable, auditable -designed for developers, governance teams, and standards bodies who need reliable behavioural evidence.