Smart Inspections

The clipboard problem

Traditional quality control in this industry produces a number, and the number goes into a monthly report, and the monthly report is read in a meeting where nobody can remember which room item 14 referred to. Nothing about that loop changes a building.

The waste is not the inspecting — inspecting is the right instinct. The waste is that the finding and the fix live in different systems, connected only by somebody’s memory and goodwill.

An inspection is a task generator

Here, failing an item does one thing immediately: it creates a corrective task, with an owner, a deadline and the photo attached. The finding and the fix are the same record.

That has a consequence worth being explicit about — failing an item is more work for the inspector, not less. They are the one who then has to see it through. We consider that correct. An inspection process where the path of least resistance is to pass everything is a process that will pass everything, no matter what the training says.

Closing the loop requires a second look. The person who did the work does not close their own finding; the item reopens for re-inspection and passes only when it has been seen again. This is the same discipline described in corrective actions.

Scored per area type, not per building

A generic checklist applied to every room produces the specific failure of being wrong everywhere. An exam room, a locker room, a loading dock and a boardroom do not fail in the same ways, and a form that treats them alike measures whichever of them it was actually written for.

So checklists are per area type, with weighting that reflects consequence: in a medical space, high-touch surfaces carry weight that a scuffed baseboard does not. The scoring runs on the digital checklists module — the same tablets and the same queue the cleaners already use, not a parallel audit system.

Why the rigour actually matters

The reason inspection scoring is strict here is not that we enjoy paperwork. It is that these scores are the training signal for everything else on this page.

AI prediction learns what an area looks like before it fails by comparing forecasts against inspection outcomes. Risk detection finds recurrence by reading inspection history. If the scores are soft, the model learns that everything is fine, and it will keep predicting that a building is fine while people in it are complaining.

Sloppy inspections do not just produce a bad report. They corrupt the forecast for months.

What it feeds

Failed items become tasks in corrective actions. Scores roll into Protex Verified™ alongside client feedback, so certification reflects both what we measured and what you experienced. And the whole history becomes the evidence base for proof of service.