Call center ai · Production
Verint Non-Emergency Call Automation for 911 and Public Safety Operations
The problem
Public safety answering points are burdened by high volumes of non-emergency calls such as noise complaints, pothole reports, and parking violations, increasing call-taker workload and contributing to dispatcher burnout.
Workflow diagram · grounded in source
1
Citizen places routine call
trigger
“routine public safety calls, like noise complaints, pothole reports, and parking violations”
2
Non-emergency call re-routed
routing
“re-routes routine public safety calls, like noise complaints, pothole reports, and parking violations, away from 911 call takers”
3
Citizen self-serves
output
“enabling citizens to self-serve on non-emergency, routine requests”
Reported outcome
The solution helps reduce call volume, creates faster response times, and eases dispatcher workload by enabling citizens to self-service non-urgent issues through automated options.
Reported metrics
Call volumereduce call volume
911 response timefaster response times
Dispatcher workloadease dispatcher workload
Reported stack
Verint
Source
https://www.verint.com/public-sector/public-safety/non-emergency-call-automation/
Read source ↗Frequently asked questions
What did this team achieve with this AI workflow?
The solution helps reduce call volume, creates faster response times, and eases dispatcher workload by enabling citizens to self-service non-urgent issues through automated options.
What tools did this team use?
Verint.
What results were reported?
Call volume: reduce call volume; 911 response time: faster response times; Dispatcher workload: ease dispatcher workload (source-reported, not independently verified).
How is this call center ai AI workflow structured?
Citizen places routine call → Non-emergency call re-routed → Citizen self-serves.