What if users could describe what they need instead of configuring how to get it?

Full Fabric holds recruitment, admissions and student records for universities. That is a lot of information, spread across many screens.

Traditional interfaces ask users to find the right screen, set up the right configuration and read the result themselves. AI opens a second route: say what you want, and let the software work out the rest.

The question was never simply how to add AI. It was which parts of an existing workflow get more useful when users can state their intent directly.

Each of the three features answers that for a different workflow, and each needs different care: one reads data, one evaluates applications, one reacts to behaviour.

AI Console

Product development of a context-driven conversational agent. Users ask questions about the data in Full Fabric in natural language instead of navigating screens or piecing information together by hand.

AI Applicant Scorer

Product development of configurable applicant evaluation. Admissions staff describe the attributes they are looking for in a prompt. When an application is submitted, it is scored against those attributes.

AI Lead Scorer

Product development of automatic scoring for prospective students, based on their activity in the platform. It turns behaviour into a score recruitment teams can act on, instead of a report to read.

The shift from fixed settings to natural-language configuration. In the Applicant Scorer, staff write the criteria themselves, so a new variation doesn't need new software development.

AI Console: platform data you can question in plain language.

AI Applicant Scorer: scoring criteria configured through a prompt, applied when an application is submitted.

AI Lead Scorer: prospective students scored automatically from their platform activity.