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Awaab’s Law Phases 1 & 2: Comply with confidence, relieve service & staff pressure

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Comply with confidence. Reduce service pressure. Protect residents and your reputation.

Awaab’s Law makes accurate, early triage critical. Housing teams must quickly distinguish between cases that are outside scope, significant hazards and genuine emergencies—without allowing overstated demand to overwhelm the service or serious risks to be missed. Connect‑AI uses intelligent triage to help staff understand the issue before attending, arrive with the right skills, materials and equipment, and resolve cases faster. This reduces unnecessary visits and means residents spend less time waiting at home for appointments.

Together with Repairs‑AI and Risk‑AI, Connect‑AI supports a proactive approach to Awaab’s Law by identifying emerging risk, reducing repeat and avoidable demand, and preventing repairs backlogs from growing. Integrated case management can also manage the complete hazard journey where existing repairs or housing systems do not provide the required functionality.

This can be extended through our omnichannel engagement capability, allowing residents to report potential hazards via WhatsApp, SMS, Telegram, email and other digital channels. Supported by an AI Hazard Agent, it gives housing providers a 24/7 reporting and initial triage capability.

Intelligent Triage: Identify Risk. Prioritise Demand. Act Faster.

Awaab’s Law makes accurate, early triage essential. Housing teams must quickly distinguish between cases that are outside scope, significant hazards and genuine emergencies—without allowing overstated demand to overwhelm the service or serious risks to be missed.

Phase 2 significantly increases the complexity of triage by introducing a wider range of hazards, each requiring different questions, evidence and risk considerations. Connect‑AI uses hazard-specific dynamic scripting, structured questioning and real-time risk scoring to guide staff through every assessment, ensuring the right information is captured and cases are triaged consistently.

Combined with Remote Video Assistance and connected resident, property and repairs information, this helps teams make faster, better-informed and more defensible decisions. It also creates a simpler customer experience by capturing the hazard, supporting evidence, vulnerabilities and next steps in one guided conversation—reducing hand-offs, unnecessary visits and the time residents spend waiting at home for appointments.

Reducing Operational Pressure While Meeting Awaab's Law

Awaab's Law has fundamentally changed operational demand. Most social landlords simply don't have the surveying capacity to physically inspect every reported damp & mould case within increasingly demanding timescales. Engage-Me helps housing teams prioritise risk, remotely assess appropriate cases, identify hidden damp & mould, and focus limited surveying resources where they are needed most.

Intelligent Triage

Use Connect‑AI to remotely assess reported hazards and distinguish between cases that are outside scope, significant hazards and genuine emergencies. Prioritise the highest-risk residents while preventing overstated demand from overwhelming the service.

See sooner. Triage faster. Act earlier.

Dynamic Scripting & Staff Support

Guide staff through the complexity of Phase 1 and Phase 2 using hazard-specific dynamic scripting, structured questions and real-time risk scoring. Capture the right information consistently while supporting—not replacing—staff judgement.


Ask the right questions. Capture the information that matters. Make consistent decisions.

24x7 Digital "Front Door"

Enable residents to report potential hazards through WhatsApp, SMS, Telegram, email and other digital channels. An AI Hazard Agent guides the resident, captures the essential information and supports initial triage at any time.

Always available. Inclusive by design.

Connected Intelligence

Bring together repairs history, complaints, previous cases, property information and resident vulnerabilities. Give staff access to all available information and the context needed to make faster, better-informed and more defensible decisions via our AI Hazard agent.

Connect the information. Understand the risk.

Case Management 

Manage the complete hazard journey from initial report and triage through investigation, repair, resident communication and post-remediation verification. Use Connect‑AI case management where existing repairs or housing systems cannot support the required process.

Manage every case. Evidence every action.

Predictive Risk Detection & Active Listening

Use Risk‑AI to identify hidden and emerging hazards across repairs, complaints and property data. Combine property risk with resident vulnerability to prioritise earlier intervention and help prevent harm before a formal case is reported.

Identify earlier. Intervene sooner. Prevent harm.

"Upon becoming aware of a potential hazard, social landlords should use all available information to initially determine if the hazard is a potential significant or emergency hazard..."

— Ministry of Housing, Communities and Local Government, Awaab's Law: Guidance for Social Landlords – Timeframes for Repairs in the Social Rented Sector (Section 1.4)

The Regulations Set the Floor. We Help You Go Beyond Them.

Awaab's Law has changed the way social landlords must think about damp and mould. Compliance is no longer just about responding to reported cases within prescribed timescales. It is about using all available information to identify, assess and prioritise housing risk before harm occurs.

Engage-Me helps social landlords operationalise that requirement by connecting property, resident and operational intelligence with AI-powered analysis and Remote Video Assistance. The result is earlier intervention, stronger evidence, more informed decision-making and a more proactive approach to protecting residents.

AI Powered Software For Social Housing

Engage Me Group Ltd

71-75, Shelton Street, Covent Garden, London, United Kingdom, WC2H 9JQ

t: 0330 1339769 

e: info@engage-me.co.uk

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