Challenge
High inbound volume and manual triage workflows were creating long response times and uneven tenant experience.
Housing
Client: Stonewater
Implemented AI-assisted tenant enquiry handling and repairs triage to improve response speed and service consistency.
Project Snapshot
Live delivery proof-33%
First-response time
+21%
Resolution consistency
-27%
Manual triage effort
Client: Stonewater
Date: 17 Apr 2025
Engagement: Service operations transformation
Duration: 12 weeks
Delivery Team: 1 programme lead, 1 workflow specialist, 1 data engineer, 1 service quality analyst
Challenge
High inbound volume and manual triage workflows were creating long response times and uneven tenant experience.
Approach
We built a structured enquiry triage model with repairs categorisation, urgency scoring, and escalation pathways for vulnerable tenants.
Impact
Service teams reduced queue pressure while improving decision quality and transparency across tenant support operations.
Implementation Narrative
Detailed delivery breakdown for Stonewater.
The organisation managed repairs, tenancy support, and community service enquiries across multiple regions. Contact volumes were high and often included incomplete information, making effective triage difficult.
Service leaders gained a clearer, data-driven view of support workload and escalation pathways. Staff handled more volume without compromising safeguarding and quality processes.
Future scope includes self-service appointment flows and predictive repairs demand planning.
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