call_center_ai · saas · workflow
Spare Labs deploys Retell AI voice agents to automate public transit call centers
Transit agency call centers serving paratransit riders with disabilities were chronically understaffed and outsourced to third parties, resulting in long hold times that prevented riders from booking trips or planning their day.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Rider initiates call
A rider calls the transit agency to book a trip, check an ETA, or get a service update.
Tools used
RetellSpare
Outcome
Within the first week of deployment, Spare's voice agents handled around 30% of inbound calls, immediately cutting hold times. Average call resolution now sits close to 70%, reaching close to 100% for simpler operations. The AI product line grew over 10x year-over-year, and agents successfully absorbed a record snowfall service surge that would have overwhelmed a human call center.
What failed first
Spare's 2023 attempt to integrate AI voice models did not meet quality requirements and the project was shelved.
Results
Time savedaround 30%
Volumeclose to 70%
Running sinceMarch 2024
Grounding & classification
Source type: vendor customer story
29 fields verified against source quotes.
ai agentconversational aivoice aicall recordingfailure mode describedhuman review describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedgovernmentsoftwareautomation ratedeflection rateresolution time reductionvendor customer storycall center aicustomer supportautonomous resolutionescalation workflowvoice call handling