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FID Case Study: Alix
Project Alix is transforming how damp, mould and condensation (DMC) issues are identified and prioritised in social housing using AI-powered triage and resident-led data capture.
Through the Future Industries Demonstrator (FID) at Queen Elizabeth Olympic Park, Project Alix has shown how real-time AI insights can reduce delays, improve decision-making and give residents a stronger voice in the management of their homes.
The challenge
Damp, mould and condensation remain a persistent issue across social housing, with serious impacts on both property conditions and resident health.
Yet identifying and prioritising cases is often slow, resource-intensive and inconsistent, with challenges including:
- Time-consuming manual triage processes
- Limited visibility of the issue before surveyor visits
- Inefficient scheduling and repeat appointments
- Inconsistent data quality across cases
- Barriers for residents in reporting and communicating issues
For landlords and contractors, this makes it difficult to respond quickly, allocate resources effectively and build trust with residents.
The solution
Project Alix has developed a custom AI platform that enables faster, more accurate triage of housing issues.
Residents are sent a simple WhatsApp link, where they:
- Answer a guided set of questions
- Upload images of affected areas in their home
The AI then:
- Analyses images and conversations in real time
- Generates a structured triage report
- Shares insights directly with contractors and landlords via a dashboard
By combining automation with resident input, Project Alix replaces manual processes with faster, more consistent and data-rich decision-making.
Testing at Queen Elizabeth Olympic Park
Through FID, Project Alix partnered with contractor JAG to test its platform in a live housing context, integrating the tool directly into existing workflows.
Trial approach
- Deployment of the AI triage tool via WhatsApp for residents
- Integration of outputs into contractor and landlord workflows
- Use of real cases to test accuracy, usability and operational value
- Continuous iteration of the product based on live feedback
Results and impact
The trial demonstrated strong performance across both operational and user outcomes:
- 87% triage accuracy, validating the effectiveness of the AI model
- 4.7/5 resident satisfaction rating, showing strong engagement and usability
- High levels of participation, with most residents providing multiple images and detailed context
- Improved workflows for schedulers and surveyors, particularly when image evidence was embedded directly into communications
- Reduced unnecessary visits and more efficient case prioritisation
The trial also uncovered opportunities to further strengthen the model, particularly in handling complex cases and improving activation across different resident groups.
