customer_support · saas · workflow
Breathe boosts Fin AI resolution rate from 56% to 82% in under a year
Breathe's existing custom bots and knowledge base failed to deflect most customer queries, with the majority still reaching live agents. The support team was flooded with repetitive how-to questions, preventing agents from focusing on complex issues, while the business needed to scale support without growing headcount.
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 arrives
Customers submit support queries, with Fin involved in 62% of the approximately 7,000 monthly conversations.
Tools used
Fin AI AgentFin Guidance
Outcome
In under a year, Breathe increased Fin's resolution rate from 56% to 82%, with Fin involved in 62% of approximately 7,000 monthly conversations. CSAT scores consistently hit 85–90%, equal to agent CSAT ratings, and Fin now resolves up to 88% of queries.
What failed first
Custom bots linked to the knowledge base did not prevent most queries from reaching the live support team.
Results
Time savedaround 7,000
Volume56% to 82%
Grounding & classification
Source type: vendor customer story
32 fields verified against source quotes.
conversational aiknowledge searchsupport agentchat transcriptknowledge basehuman review describedmetric backednamed customerproduction runtime claimedsource backedtools describedworkflow describedsoftwareautomation ratecustomer satisfactiondeflection rateemployee productivityvendor customer storycustomer supportticket triageautonomous resolutionescalation workflowrag answering