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Closing The Housing Risk Intelligence Gap
Awaab's Law has fundamentally changed the role of social landlords. The challenge is no longer simply responding to reported damp & mould cases—it is using all available information to identify, assess and prioritise housing risk before harm occurs.
At the same time, the Consumer Standards place greater emphasis on understanding tenants' needs, protecting vulnerable residents and taking a proactive approach to safety and service quality. With Phase 2 of Awaab's Law extending statutory requirements across all HHSRS hazards, the pressure to identify hidden and emerging risk before it is reported will only continue to grow.
Yet some of the highest-risk cases remain invisible. Repairs history, complaints, missed appointments, resident vulnerability, property condition and service interactions all contain early warning signs, but they are typically spread across disconnected systems, managed by different teams and viewed in isolation.
Risk-AI closes this Housing Risk Intelligence Gap by connecting resident, property and operational intelligence, enriching it with external data and predictive risk models, and continuously identifying emerging risk that would otherwise remain hidden. The result is earlier intervention, more consistent decision-making and a proactive approach to preventing avoidable harm.
Turning Connected Data Into Earlier Intervention
Preventing harm requires more than analysing historic repairs data. It demands a connected understanding of properties, residents and place in real-time. Engage-Me combines operational data with external datasets, predictive AI models and Connected Housing Intelligence to identify emerging housing risks, prioritise intervention and provide operational teams with actionable intelligence before harm occurs.
Closing the Housing Risk Intelligence Gap
The challenge facing social landlords isn't a lack of data. Every day, repairs, complaints, inspections, housing management systems, resident engagement and asset information generate vast amounts of operational intelligence. The challenge is bringing that information together, understanding it at scale and turning it into actionable insight before harm occurs.
Engage-Me connects internal systems with external intelligence and AI-powered predictive modelling to identify patterns, surface hidden risks and prioritise intervention—transforming disconnected data into Connected Housing Intelligence.
Active Risk Intelligence
Continuously analyse repairs, complaints and contact data to identify emerging housing risks before they escalate into service failure, regulatory intervention or resident harm.
Identify emerging risk before it's reported.
Hidden Risk Detection
Identify "silent voices", repeat repairs, recurring complaints and emerging patterns that may indicate hidden damp & mould & wider housing hazards before they are reported.
Find the risks hidding in your data.
Resident Vulnerability Intelligence
Understand how resident vulnerability changes the severity and urgency of housing risks. Prioritise intervention based on both property condition and the people living within it.
Identify & help those who need supported the most.
Operational Risk Prioritisation
Automatically create prioritised work queues for managers, surveyors and operational teams, ensuring limited resources are directed towards the highest-risk residents and properties.
Turn insight into action.
From Data Rich to Intelligence Led
The future of social housing isn't about collecting more data—it's about turning information into intelligence and intelligence into earlier action. Organisations that can identify risk sooner, prioritise intervention more effectively and protect their most vulnerable residents won't just comply with regulation—they'll deliver safer homes and better outcomes for the people they serve.