Systems for testing and evaluating AI products.

Context

AI systems do not fail like ordinary software. They rarely throw an error; they produce something plausible that is wrong, or subtly worse than last week. Conventional testing catches almost none of this, because there is no single correct output to assert against.

This experience involved building the systems and workflows used to test and evaluate AI chatbots.

What evaluation work actually involves

• A representative case set. Real inputs, including the awkward and ambiguous ones, with an agreed view of what a good response looks like. Assembling this is the largest part of the work and the part most often skipped.
• Defining quality. Correctness, completeness, tone, refusal behaviour and handling of uncertainty are separate dimensions. Collapsing them into one score hides the regressions that matter.
• Repeatability. Evaluation has to run the same way on every change, or comparisons between versions mean nothing.
• Automated and human judgment together. Automated scoring gives coverage and speed; human review catches what automation misses. Both are needed, and the boundary between them should be deliberate.
• Regression detection. The practical question is not “is it good” but “did this change make it better or worse, and for whom”.
• Production monitoring. Evaluation continues after launch, because inputs drift and models change underneath you.

Why this matters for enterprise AI

Evaluation is what separates an AI pilot from an AI system. It is how a business decides whether to extend automation, where human review is still required, and whether the vendor's latest model upgrade actually helped.

We apply the same discipline to the AI applications we build for clients.