What Decagon does well
Strong on high-ticket-volume use cases (Notion, Vercel, Bilt). Mature workflow authoring. Custom escalation taxonomies.
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Content is loadingDecagon ai agents for high-volume support teams. Here's where they're a better choice than us — and where we're a better choice than them. We score against our own matrix so you can argue with it.
Strong on high-ticket-volume use cases (Notion, Vercel, Bilt). Mature workflow authoring. Custom escalation taxonomies.
Per-month or per-resolved pricing that requires upfront commit. No dialect specialization. No outcome-quality gates (no CSAT clawback in their pricing).
Workflow-authoring UI maturity. Bigger investment in case-management features on top of the agent.
We mark a row "Fahmix AI" where we believe we beat them today, "Decagon" where they beat us today, and "Tie" where it's commodity parity.
| Capability | Fahmix AI | Decagon | Edge |
|---|---|---|---|
| Pricing model | Per ROU (~$0.50–$0.80, includes model costs) with CSAT + reopen clawback | Custom commit + per-resolved with no automatic clawback | Fahmix AI |
| Workflow authoring | AOP authoring with deterministic compiler + tool whitelist | More mature visual UI | Decagon Decagon's flow builder UI is more polished today. |
| Native Banglish / Hinglish | Day-1, judge-evaluated in CI | English-first | Fahmix AI |
| Voice + MCP day-1 | Yes | Voice yes; MCP partial | Fahmix AI |
| Time to first conversation | < 1 hour self-serve | Implementation team led | Fahmix AI |
| Data residency | Per-tenant | US-primary | Fahmix AI |
First 100 ROUs are free. If we lose against Decagon for your use case, you'll see it inside an hour.