Proof

Proven analytics, newly applied to insurance

We are honest about what is proven and what is new. The detection engine has a published track record on live industrial sites. Applying it inside insurance products is the new part, and that is exactly the opportunity.

A real claim

Two workers on scaffolding were messing around. One pushed the other, who fell into the safety net and was unharmed. A second incident followed. That time the net failed, and a worker lost their life.
The first incident was a warning that nobody saw. Catching exactly this kind of unsafe behaviour, early enough to act, is what the technology below already does on live sites every day.

Tier 1 · The engine's track record

The detection engine is already proven on live sites

Mitigate It does not build the AI. The detection engine is developed by our technology partner, a specialist video analytics company whose platform runs on industrial sites internationally, across construction, logistics, marine and aviation operators. These are the developer's own published figures.

400+ projects delivered with the engine Technology partner, published
15 countries deployed, across four continents Technology partner, published
4.2m+ hazards detected since launch Technology partner, published
50+ configurable detection use cases Technology partner, published

73% reduction in recordable incidents

The developer reports a 73% reduction in total recordable incident rate where the platform is deployed.

Source: technology partner, published figures

84% fewer safety non-compliances

Continuous monitoring drives an 84% reduction in safety non-compliances, as unsafe behaviour is caught and corrected early.

Source: technology partner, published figures

50% fewer manual inspections

Continuous AI monitoring reduces the manual inspection burden on site teams by up to half.

Source: technology partner, published figures

All figures in this section are our technology partner's own published figures from their deployments; the source is shared with insurers under consideration. They are the engine's results on industrial sites, not Mitigate It's insurance results.

Tier 2 · The market evidence

The perils are huge, and prevention already pays

The risks these cameras watch for are among the largest and most frequent in UK commercial insurance, and insurers already fund prevention technology where it cuts claims.

£1.8m paid out every day by UK insurers on escape of water ABI
£6.1bn UK property insurance payouts in 2025 ABI
£22.9bn a year, the cost of workplace injury and ill health in Britain HSE, 2023/24
28% of UK worker deaths are falls from height, the single leading cause HSE, 2024/25

Prevention already pays for insurers. A leading UK insurer gives every buildings-insurance customer a free leak detector, and insurer leak-detector programmes report around 39% fewer water-damage claims. The same logic, fund prevention and cut claims, is what AI CCTV extends across fire, safety and security.

Sources: ABI, HSE and LeakBot.

Tier 3 · The opportunity

Proven on site. Not yet applied to insurance. That is the opening.

No UK insurer currently embeds AI CCTV analytics in a commercial product. The engine is proven, the perils are quantified, and the precedent of insurers funding prevention is established. What does not exist yet is the insurance application, which means the advantage goes to whoever moves first. Mitigate It exists to bring the two together, and as our deployments mature we will share our own results, references and case studies with insurers under consideration.

Being straight with you: the figures on this page are our technology partner's published results and industry statistics. They show the proven impact of the approach and the scale of the perils. They are not Mitigate It's own insurance results, because no insurance deployment exists yet. That gap is the first-mover opportunity.

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See it working on a real site

A focused 30 minutes, not a slide deck: live detections, the risk data your underwriters would receive, and what a pilot could look like on part of your book. No obligation.