customer_support · healthcare · workflow
Numan scales safe, AI-first patient support to 47% resolution rate with Fin AI Agent
Numan's support volume surged to nearly 70,000 conversations a month, spanning delivery queries to sensitive clinical topics. Their existing platform could not scale intelligently, requiring the team to manually manage every type of query while ensuring medical questions were safely routed to licensed clinicians.
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 · Customer query received
Customer support queries arrive spanning topics from delivery updates to sensitive questions about treatment plans.
Tools used
FinFin GuidanceIntercom
Outcome
Fin autonomously resolved 47% of conversations, saved 19,000 hours annually (the equivalent of nine full-time agents), and maintained a 90% CSAT score on par with human agents.
What failed first
Numan's previous customer service platform lacked the intelligence to handle growing volumes, and competing AI tools they evaluated failed in real-world testing despite performing well in demos.
Results
Time saved19,000 hours
Volume47%
Cost replacedaround four pounds
Grounding & classification
Source type: vendor customer story
33 fields verified against source quotes.
ai agentconversational aiknowledge searchsupport agentchat transcriptknowledge basefailure mode describedhuman review describedmetric backednamed customerproduction runtime claimedtools describedvendor confirmedworkflow describedhealthcareautomation ratecost reductioncustomer satisfactiondeflection rateemployee productivitytime savedvendor customer storycustomer supportautonomous resolutionescalation workflow