
Triaging maintenance requests around the clock
A voice and messaging assistant identifies urgent property issues, gathers evidence and coordinates the correct response.
A realistic starting point.
Tenants report maintenance through calls, texts and emails with inconsistent detail. After-hours managers struggle to separate genuine emergencies from work that can be scheduled the next day.
A digital employee built around the work.
ANNA captures the property, issue, access conditions and photographs, then applies manager-approved urgency rules. Critical incidents alert the duty manager; routine work becomes a structured request for the appropriate contractor queue.
From first contact to a useful outcome.
- 01
Identify the property and authorised contact
- 02
Ask issue-specific safety and damage questions
- 03
Collect photographs or video when appropriate
- 04
Apply deterministic urgency categories
- 05
Notify the correct duty person or supplier queue
- 06
Keep the tenant updated through the chosen channel
What a well-run scenario could change.
Planning estimates only. Not a promise, forecast or report of actual AIIT client performance.
Emergency acknowledgement
Manager triage time
Tenant status calls
The conditions behind the model.
These boundaries make the scenario interpretable. Change the demand, workflow mix, integration quality or adoption rate and the potential outcomes change as well.
Urgency categories are set by the property manager
The system does not diagnose building faults
Supplier dispatch requires approval unless explicitly pre-authorised
Tenant communications use current consent records
How this scenario could be implemented.
Analyse the most common maintenance categories
Agree safety questions and emergency triggers
Connect property records and contractor queues
Launch after hours before extending to all channels
What matters beyond automation.
Photographs reduce clarification but should never delay a safety escalation.
Tenants value proactive status updates after the initial report.
Property-specific context prevents avoidable questions.
About this scenario: AIIT created this fictional, anonymised operating example to support planning conversations. The figures are calculations based on the assumptions shown above; they do not represent an existing client, testimonial or guaranteed result.
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