Kalshi, whose support volume grew from 30,000 tickets in 2024 toward a projected two million by end of 2026, uses Fin. Intercom reports Fin resolves around 80% of conversations end to end.
Reported outcomes
automation rate
around 80% resolved end to end
Source [1] · self-reported
Outcomes are reported exactly as stated by the source; we have not independently audited them.
How it works
- Fin answers inbound support conversations using the company's help content.
- Hands unresolved conversations to human agents.
Risks & compliance
- Incorrect answers to customers; human review and escalation paths needed
- Vendor-published results; not independently audited
- Financial-services disclosures and account security
Build this yourself
Tools mentioned & how to build it
Disclosure: Some links may earn us a commission; it never affects grades. Tools are listed by fit to this case, never by payment.
Tools mentioned
- Fin AI Agent (Intercom)
Intercom's customer-service AI agent; the most-documented tool in the casefile.
Build a version yourself
- HubSpot (Breeze agents)
SMB-friendly CRM with Breeze AI agents for prospecting and support. - n8n
Open-source workflow automation with AI agent nodes; self-host or cloud. - Relevance AI
Build an 'AI workforce' of agents, e.g. BDR agents that research and book meetings.
Sources
“Fin is now involved in nearly every support conversation and resolves around 80% of them end to end, without a human stepping in.”How Kalshi automated 80% of customer support with Fin
Grade rationale: Named organization with a quantified outcome from the vendor or the organization itself (self-reported). Automated check on Oct 17, 2026: 1/1 quotes and 1/1 outcome claims matched the source. Compiled by the Agent Casefile research desk (AI-assisted; see methodology). Spot an error? Request a correction.
