Cut repair times in half with AI-powered triage
Your AI employee receives every repair request — phone, email, portal — classifies urgency against your policy matrix, checks real-time contractor availability, books the slot and confirms with the tenant. No more backlogs. No more missed appointments.
The challenge
Why this workflow is broken today
Manual classification delays response
Housing officers spend hours sorting repair requests by urgency. Emergency jobs get buried behind routine ones, and tenants wait days for an initial response — eroding satisfaction scores.
Contractor scheduling is a bottleneck
Matching the right trade to the right job at the right time across dozens of suppliers means constant phone tag. Double-bookings and no-shows waste tenant goodwill and budget.
No single view of repair status
Data lives across spreadsheets, legacy systems and email inboxes. Managers can't see what's overdue, what's pending parts, or which contractors are consistently late.
How your AI employee handles this
From request to resolution
Receive and classify
The AI employee monitors every inbound channel — phone transcripts, portal submissions, emails — and classifies each request against your urgency matrix (P1 emergency through to P4 planned). Natural language understanding catches the details tenants describe informally ('the ceiling is dripping') and maps them to the right category.
Match and schedule
Using your contractor roster and real-time availability feeds, the AI employee identifies the best-fit tradesperson based on skill, location, SLA status and cost. It proposes available slots to the tenant and confirms the booking — all within minutes of the original request.
Confirm and track
The tenant receives a confirmation with the appointment window, contractor name and what to expect. The AI employee then tracks the job through to completion — following up with the contractor if overdue, rescheduling if cancelled, and logging every status change in your housing management system.
Report and learn
After each completed repair, the AI employee logs first-time fix rate, time-to-completion and tenant feedback. Over time, it learns which contractors deliver and which ones don't — recommending roster changes and flagging recurring property issues before they become expensive.
Scenario
How Oakbridge Housing would use this
Organisation
Oakbridge Housing
A Midlands-based housing association managing 18,000 homes across 12 local authorities, with an in-house maintenance team of 45 operatives supplemented by 60+ external contractors.
Challenge
Oakbridge was processing over 3,200 repair requests per month, with an average first-response time of 4.2 days and a completion target they were missing 38% of the time. Their repairs team of 8 coordinators was overwhelmed, leading to high staff turnover and declining tenant satisfaction scores (TSM repairs satisfaction at 58%).
Approach
The AI employee was onboarded to Oakbridge's repair categories, contractor roster, and scheduling system over two weeks. It began handling triage and scheduling for non-emergency repairs first, with human oversight for the first month. After validation, it was extended to all repair categories including emergencies.
Projected results
“We went from firefighting every morning to actually managing repairs proactively. The AI handles the volume so our team can focus on the cases that need a human touch.”
— Head of Property Services, Oakbridge Housing (representative scenario)
This is a representative scenario based on typical client profiles. Specific results vary by organisation.
Frequently asked questions
Common questions about this workflow
How does the AI handle emergency repairs differently?
Emergency repairs (P1) follow a separate, accelerated workflow. The AI employee recognises emergency keywords and patterns, immediately escalates to your emergency contractor, and can trigger out-of-hours protocols. Human oversight is maintained for emergencies — the AI handles the initial triage and scheduling while alerting your on-call team.
Does it integrate with our existing housing management system?
Yes. We integrate with all major HMS platforms including Civica Cx, NEC Housing, MRI Software, Aareon QL, and others. The AI employee reads from and writes to your existing system — it doesn't replace it. If you use a bespoke system, we build a custom integration during onboarding.
What happens when a repair request is ambiguous?
When the AI employee can't confidently classify a request, it asks the tenant one or two clarifying questions (via their preferred channel) before proceeding. If it still can't classify, it escalates to a human coordinator with a summary of what it knows — so the human starts with context, not a blank screen.
How long does onboarding take?
Typically two to three weeks. During week one, we ingest your repair categories, contractor roster, SLA policies and scheduling rules. Week two is a supervised pilot on a subset of requests. By week three, the AI employee is handling the full volume with your team reviewing edge cases.
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