customer_support · finance · workflow
Swyftx achieves 48.5% increase in automated resolution rate with Intercom Fin AI Agent
As Swyftx's user base surged, the volume and complexity of customer inquiries overwhelmed traditional support systems, and previous-generation rule-based bots failed to handle complex questions, creating customer frustration.
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
A customer query of varying complexity arrives at Swyftx's support channel.
Tools used
FinIntercom
Outcome
Fin AI Agent achieved a 49% resolution rate (a 48.5% increase over the previous solution), a 91% answer rate, and saved Swyftx's support team over 40 hours per week, with sentiment towards Fin improving dramatically.
What failed first
Previous-generation chatbots built on rigid, rule-based structures could not handle complex questions, and customers were frustrated because they did not know the parameters of these rules.
Results
Time saved40+ hours per week
Volume48.5%
Grounding & classification
Source type: vendor customer story
27 fields verified against source quotes.
ai agentconversational aisupport agentknowledge basesupport ticketfailure mode describedhuman review describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedfinancial servicesautomation ratecustomer satisfactiondeflection ratetime savedvendor customer storycustomer supportautonomous resolutionescalation workflow