customer support · pattern

Voice AI agents

Voice-first AI agents handling inbound calls, outbound campaigns, or in-app voice experiences.

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 · Call ingress & channel routing
Phone hotline, web, mobile app, and chat-voice channels normalize into one stream; routing picks the right agent configuration per market or language.
What fails first / common problems

Recurring first-deployment failures from the matching workflows'what_failednotes. First sentence of each, attributed to the source case.

Aspire's legacy live chat vendor went out of business, forcing a platform change.
The existing IVR system was outdated, rarely updated, and unable to retain callers in self-service, sending the majority to expensive outsourced live agents.
Revolut's internal AI build validated the concept but could not be productionized at scale due to the complexity of speech-to-text, LLMs, TTS, real-time turn-taking, PCI compliance, and zero-retention controls.
Previous technology was described as anemic with major reporting problems, and quality management was effectively arbitrary—only a handful of random calls were reviewed each month.
Gorgias, Makesy's previous CRM, had a confusing tagging system that made it impossible to accurately determine why customers were reaching out, and offered no insight into agent availability or scheduling.
Tools commonly seen
elevenlabselevenagentsagent assistbesidecrestallmsasrelevenlabs agentsretell aisynthflowtext to speechai agent
Representative outcomes

Real metrics from selected cases — verbatim from each workflow'snumberspanel. Click any title to open the full case.

Example workflows

Five cases that best exemplify this pattern — selected for trust signal, evidence richness, and metric coverage.