Customer support · Production

Peddle saves $163K a year with Fin AI Agent, cutting Peddler Support chat volume by 64%

The problem

Peddle's support operation faced growing inquiry volume across multiple workspaces, pulling supervisors and quality leads away from higher-value work. Scaling to meet demand would have required hiring additional headcount, threatening the team's lean, efficient culture.

Workflow diagram · grounded in source
1
Inquiries arrive across workspaces
trigger
“Seller Support (for customers), Peddler Support (for internal agents), and Carrier Support (for network partners)”
2
Fin deflects and resolves chats
ai_action
“Fin started deflecting chats immediately. We couldn't believe how fast it worked.”
3
QA team reviews flagged conversations
human_review
“Our QA team also flags conversations where Fin 'got it wrong,' but more often than not, it's a misspelling or vague question. Fin answered what it could. We use that as a coaching opportunity.”
4
Content and coaching loop
feedback_loop
“We update articles constantly. We coach agents on using Fin during screen monitoring. It's built into our DNA.”
5
Chat volume reduced, hours freed
output
“64.1% reduction in chat volume in Peddler Support”
Reported outcome

Deploying Fin across Seller and Peddler Support workspaces cut chat volume by 64.1% in Peddler Support, saved 899.63 hours per month, avoided $163,800 in annual costs (2.5 FTE equivalents), and achieved a 70% resolution rate.

Reported metrics
chat volume reduction (Peddler Support)64.1%
Hours saved per month899.63 hours/month
Annual cost avoidance$163,800
Full-time hires avoided2.5
Show all 7 reported metrics
chat volume reduction (Peddler Support)64.1%
hours saved per month899.63 hours/month
annual cost avoidance$163,800
full-time hires avoided2.5
AI resolution rate70%
overall chat volume reductioncutting volume by more than half
hours freed for leadershundreds of hours
Reported stack
FinIntercomSalesforce
Source
https://www.intercom.com/customers/peddle
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Deploying Fin across Seller and Peddler Support workspaces cut chat volume by 64.1% in Peddler Support, saved 899.63 hours per month, avoided $163,800 in annual costs (2.5 FTE equivalents), and achieved a 70% resoluti…

What tools did this team use?

Fin, Intercom, Salesforce.

What results were reported?

chat volume reduction (Peddler Support): 64.1%; Hours saved per month: 899.63 hours/month; Annual cost avoidance: $163,800; Full-time hires avoided: 2.5 (source-reported, not independently verified).

How is this customer support AI workflow structured?

Inquiries arrive across workspaces → Fin deflects and resolves chats → QA team reviews flagged conversations → Content and coaching loop → Chat volume reduced, hours freed.

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