Decagon provides enterprise-grade AI support agents that resolve customer issues end to end with human-like reasoning and deep system integrations.
Decagon builds AI agents designed to handle complete support workflows rather than simple FAQ deflection. The platform reasons across policy documents, order systems, and historical tickets to decide what a customer is entitled to, then executes the resolution, whether that is a replacement, a refund, or a plan change. Its analytics suite explains what the agent did and why, which gives support leaders the confidence to expand automation scope. The company targets large consumer businesses where support quality directly affects retention. Decagon pairs every deployment with evaluation tooling, so teams can measure quality against past tickets before the agent meets real customers.
Decagon contracts are structured around resolution volume and the complexity of integrated systems, with pricing provided after a technical discovery session. Engagements include onboarding, integration engineering, and ongoing optimization against agreed resolution and quality targets. Contracts typically include agreed service levels for resolution quality, not just volume.
Decagon is aimed at enterprise consumer companies in e-commerce, marketplaces, fintech, and hospitality that want AI agents to complete transactions, not just answer questions. It fits organizations ready to invest in deep integrations for durable automation gains. Support leaders who track cost per contact often see the economics improve as integrated actions replace manual back-office work.
No. Decagon is an enterprise product with negotiated pricing tied to resolution volume and integration scope.
Yes. Decagon’s agents are built to act through APIs, which is how they perform tasks like issuing refunds or modifying bookings during a conversation.
Decagon focuses on complex end-to-end resolutions with strong evaluation tooling; lighter chatbot builders are cheaper for simple FAQ automation but stall on multi-system tasks.
Decagon hands the conversation to a human with a full summary and recommended next steps, and logs the case so the gap can be addressed in the next optimization cycle.
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