Case Study

Agentic AI increases net yield by 10% at L1 Property in just 4 months.

Introduction

QED Property Management deployed the QED Labs platform and its AI Agents across one of its client's portfolios, managed on behalf of L1 Property, covering 10 residential buildings and 603 units in November 2025. Within just four months, QED Labs had transformed how maintenance is reported, diagnosed, and resolved across the entire portfolio — improving the overall net yield.

The challenge

Before implementing QED Labs, the property management team faced several operational challenges:

  1. Manual triage bottleneck: Every maintenance issue required manual diagnosis through phone calls and emails, consuming significant property management resource and creating delays.
  2. Incomplete issue information: Contractors were frequently dispatched without sufficient diagnostic detail or photos, leading to wasted visits and repeat appointments.
  3. No systemic visibility: Building-wide patterns, like recurring equipment failures, weren't being surfaced, meaning reactive fixes took priority over preventive action.
  4. Tenant communication overhead: Status updates, follow-ups, and general queries all needed manual responses, adding to an already stretched team's workload.
“QED Labs has fundamentally changed how we manage maintenance. The AI captures the right information upfront, dispatches contractors automatically, and handles follow-ups, allowing our team to focus on what matters most: delivering exceptional service to our residents.”
— David Lamm
CEO, L1 Property

Solution QED Labs delivered

QED Labs was deployed across the portfolio, providing end-to-end maintenance automation using AI:

  1. AI-Powered Diagnostic Triage: The AI conducts structured diagnostic triage, asking the right questions, requesting photos, and assessing safety risk, before automatically creating maintenance requests, dispatching contractors, and managing the full job lifecycle.
  2. Automated Contractor Dispatch: 765 jobs dispatched to 36 contractors with full lifecycle tracking, achieving a 66% increase in first-time fix rates and removing the need for manual job allocation.
  3. Intelligent Query Resolution: 44% of all tenant conversations are resolved by the AI without raising a formal maintenance request, handling status updates, general queries, and follow-up communication.
  4. Priority-Based Response: AI-driven priority triage ensures urgent P1 issues are resolved 38% faster than lower-priority work, improving safety outcomes and resident experience.

Results after 4 months

>10%

Increase in Net Operating Income (NOI)

~660 hrs

Estimated staff time saved on triage, dispatch, and follow-ups

~£12,500

Estimated cost saved (avg. UK property manager salary £35k/yr)

603

Residential units supported by an AI Agent

38%

Residents have interacted with our AI Agents (79/100 satisfaction)

752

Tenant conversations with our AI Agents

21,000+

Messages handled

78%

Of issues resolved by our AI Agents

7.7 days

Median turnaround

76%

Issues include tenant-submitted photos

44%

Queries resolved without formal request

66%

Increase in first-time fix rate

38%

Faster P1 resolution

Operational impact

The integration of QED Labs delivered measurable improvements across the entire maintenance lifecycle:

  1. Near-total adoption: Over 95% of all reactive maintenance requests on the portfolio are now handled through the system, with 38% of residents already having engaged with the WhatsApp-based AI triage.
  2. Richer diagnostic data: 76% of issues now include tenant-submitted photos before a contractor even attends, dramatically reducing wasted visits and improving first-time fix rates.
  3. Reduced team workload: 44% of conversations are resolved by AI without creating a formal maintenance request. The property management team is also freed from repetitive status updates and follow-ups, compounding the efficiency gains.
  4. Tenant satisfaction: Post-resolution feedback is automatically collected, achieving a 64% response rate confirming 79/100 tenant satisfaction.
  5. Systemic issue detection: The platform has surfaced building-wide patterns, including failing hot water controllers across multiple buildings and recurring window seal failures, giving the operations team data-driven insight into where preventive maintenance and capital expenditure will have the greatest impact.
“QED Labs has transformed our maintenance. Residents love that they can message in any language and get live updates. We now see every problem as it unfolds — giving better visibility than any system I've used.”
— Thomas Collins
COO, L1 Property

Looking ahead

The pilot has demonstrated that AI-driven maintenance triage doesn't just improve efficiency — it fundamentally changes the quality of data available for property management decisions. With structured diagnostics, automated contractor management, and systemic pattern detection, QED Property Management now has a scalable model for delivering faster, smarter, and more transparent maintenance services.

Building on these results, the team plans to expand QED Labs deployment across additional portfolios, further leveraging AI capabilities to reduce costs, improve resident experience, and enable proactive asset management across their entire property estate.

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