How we calibrate AI for accuracy

Before we rely on AI at scale, we don't assume it's right - we calibrate it against decisions and outcomes we already trust. Here's how that works across recruitment, training and QA.

The job description forms the basis of the AI's assessment of candidates, so it's essential that recruiters keep it accurate and up to date. In seconds, our recruitment tool evaluates each candidate's CV against the requirements in the job description and ranks candidates by their suitability for the role. This helps recruiters prioritise the strongest candidates while retaining full control over who progresses. To calibrate the model, we compare AI scores against real recruitment outcomes. In a recent deployment, placed candidates scored ~43% higher on average than those who were rejected. Just as importantly, the scores for placed candidates clustered tightly together, showing low variance. In other words, the AI isn't simply rating successful candidates higher - it's identifying them consistently, suggesting it's recognising many of the same indicators of quality that experienced recruiters use.

Our training simulator allows learners to interact with an AI customer by voice or chat to assess their readiness before handling live customer interactions. To calibrate it, we ask live agents from every performance quartile to use the simulator and measure retained knowledge, system navigation and handle time. The collected data shows agents who perform well with real customers also excel in the simulator, while those who face challenges in live settings often encounter similar difficulties in the simulation.

For QA, we calibrate by having the AI score a set of calls that have also been QA'ed manually and then compare the scorecards side by side. This process highlights how important it is to define the assessment criteria clearly. For example, if you tell it there should be no interruptions, it will flag every one - including brief acknowledgements such as "mhmm" or "OK". Defining what you actually mean is part of the calibration. What we consistently observe is strong alignment between AI and manual scoring, with the AI also flagging issues that are missed in manual QA.

Calibration isn't a one-off exercise - it's an ongoing process that makes AI accountable, builds trust with the teams using it and ensures every score can stand up to scrutiny. How are you assessing and calibrating the accuracy of your AI solutions?