Decagon Sells To Duolingo. We Built For DTC.
EvoAI is agent-native, built specifically for Shopify and DTC support. It resolves the ticket end-to-end and charges once, per resolution. 100% of tickets resolved: AI handles the bulk end-to-end, your team closes the rest.
of tickets resolved: AI handles the bulk end-to-end, your team closes the rest.
lower support cost vs. a human team, live in 1 day.
An enterprise SaaS platform vs. an AI employee built for DTC
Decagon sells into enterprise SaaS and consumer platforms (Duolingo, Chime, Rippling) with an implementation team behind it. EvoAI was built agent-native for Shopify and DTC brands. Here's how they line up.
Why DTC teams pass on Decagon
The same gaps, over and over, from DTC teams who evaluated Decagon. Here's how EvoAI answers each one.
| What DTC teams say about Decagon | Evo AI |
|---|---|
| Limited transparency: hard to see why the agent made a particular decision, or to tweak its behavior afterward. | Full audit trail on every action, with policy thresholds you set and can see. |
| It's trying to replace too much, too quickly, without giving teams enough control. | You stay in control: approval queues and escalation rules, not a black box. |
| It's a standalone platform. Adopting it often means replacing the helpdesk you already use. | Works alongside your existing tools. No rip-and-replace required. |
| A single generalist agent can get confused when a conversation jumps between topics. | Built around a single ticket's full context: order, history, and policy together. Live in 1 day. |
| Implementation needs dedicated Agent Engineers and real setup time before it delivers value. | No Agent Engineers required. Live in 1 day on your existing tickets and store data. |
The real question isn't cost. It's fit.
Decagon doesn't publish list pricing. Buyer-side reporting (Vendr) puts real annual contracts in a wide range depending on ticket volume and integration scope, on top of a platform-fee minimum around $50,000/yr. Here's the reported spread.
Illustrative, based on reported Decagon contract values (Vendr) plus its disclosed platform-fee floor. Enterprise deals are negotiated case by case. EvoAI publishes a usage-based rate up front, no procurement cycle required to see a number.
The difference isn't the logo list. It's the fit.
Decagon's credibility is real: Series D funding, enterprise-grade infrastructure, recognizable logos. None of that is built around a Shopify order, a return policy, or a DTC brand voice. EvoAI is: it already knows the order, detects the store and currency, checks the customer's history, and acts within your policy. Same ticket, different context, different outcome.
Built for DTC, not enterprise SaaS
Decagon's flagship logos run consumer and enterprise SaaS support at massive scale. EvoAI is built for Shopify order data, refund policy, and brand voice out of the box.
Context built in, not bolted on
EvoAI is powered by Blotout, so orders, attribution, and prior conversations are in the reply from the first message. Live in 1 day.
Priced per resolution, sized for DTC
Decagon is priced and implemented for enterprise scale. EvoAI charges once, per resolution, sized for a DTC support team, not a platform-wide rollout.
Switching from a Decagon evaluation
The questions DTC teams ask us most when they move off a per-ticket helpdesk.
Because Decagon is built and priced for enterprise-scale deployments, contracts start around a $50K/year platform fee, with median deals near $400K/year. EvoAI publishes one number: cost per resolution, sized for a DTC team, not a platform-wide rollout.
EvoAI is agent-native: it checks the order, applies your refund and exchange policy, auto-approves what's allowed, and only escalates the edge cases, with a case file ready to read.
Most brands are live in 1 day. There's no platform-wide implementation project. EvoAI learns from your existing tickets, help center, and store data.
Yes. EvoAI replies in your brand voice and operates inside the policy you set: refund limits, approval thresholds, escalation rules.
The remaining tickets land with your team as a ready-to-read case file: order, history, attribution, and what EvoAI already tried.
Want to see it on a DTC ticket queue?
Send us your monthly ticket volume and we'll show you EvoAI running on a DTC-style ticket queue like yours.