Public sector · Build a capability Illustrative scenario
Triage for a regional council’s customer requests
The situation
A regional council’s customer service team receives a high volume of requests by email and web form — permits, rates, waste, roads. Officers spend much of the day reading, sorting and routing before any real work starts, and response times vary by who picks the request up.
Scope
- Discovery workshop with customer service and two receiving departments
- AI drafts a category, urgency and suggested routing for each incoming request
- Suggested replies drawn only from approved council information
- Integration with the existing CRM — no new system for officers to learn
Where people stay in control
- An officer approves every routing decision and every reply before it is sent
- Anything involving safety, hardship or complaints goes straight to a person
- Runs in an Australian cloud region; no resident data used to train models
- Weekly sample review of AI suggestions against officer decisions
Who does what
Melora leads discovery, designs the workflow and controls, builds and integrates the assistant, and trains the team. The council provides a sponsor, subject-matter officers, CRM access and approves the governance settings.
What you receive
- Current-state workflow map and request taxonomy
- Approval and escalation matrix
- Working pilot integrated with the CRM
- Officer guide and short training sessions
- Pilot measurement report
How success is measured
Before the pilot, sample two weeks of requests to record time-to-first-action and re-routing rates; measure the same during the pilot, alongside officer feedback and the share of AI suggestions accepted unchanged.
Decision point
Extend to more request types, adjust, or stop — decided by the council on the measured results.