Decagon vs Vapi
Relationship
Decagon Voice and Composer both serve buyers who need to answer or place phone calls with a voice agent; Vapi's scale not recorded.
Assembled from the recorded fields for this pair, not hand-checked. The comparison below is read from each company’s own profile.
4 shared capabilities — Across all their products, both list agent orchestration, evaluation and observability, voice agent and 1 more.
Ludbee capability tags · every product each company sellsShared product type — Both ship AI agent and agent platform.
Ludbee product recordsAligned comparison
Capability overlap
Shared · 4
Not verified for Vapi · 5
Recorded for Decagon. Vapi’s product records say nothing either way — a missing record is not a missing capability.
Vapi has no capability Decagon lacks, among the 4 recorded here.
Products, side by side
Algorithmic pairing — assembled from recorded fields, not hand-checked
Decagon
AI agent
Builds and runs customer-support agents that resolve enquiries over chat, email and voice.
Decagon's chat channel — an AI agent that handles live customer conversations in a web or in-app chat surface.
Decagon's email channel — agents that read and answer customer email threads rather than routing them to a queue.
Decagon's voice channel: an AI agent that answers customer phone calls in place of a hold queue and, through Outbound Voice, also initiates calls to customers at the right moments in their journey.
Agent platform
Decagon's authoring layer for the rules an AI support agent follows, so non-engineering teams can build, iterate on and scale agents.
An AI partner built into Decagon that helps teams build and improve support agents — distilling best practices from hundreds of Decagon deployments into guidance for the builder, and auto-tuning the agent via "Duet Autopilot," which improves it with every conversation it handles.
No counterpart
Decagon sells these in a stack layer with no product recorded for Vapi yet — nothing on the other side to compare them against.
Application
Catalog of customer-journey modules, such as collections and onboarding, that extend a Decagon agent beyond support with the infrastructure, analytics and campaign orchestration each journey needs, managed as multiple instances of one agent.
Built-in AI chat assistant that helps business teams debug their Decagon agents by consolidating audit logs, agent reasoning and tool errors, explains why an agent behaved as it did, and guides the fix.
Control center for outbound customer campaigns run by Decagon agents: sets channels, allowed hours and retry logic, sequences follow-ups over days or weeks across SMS and voice, and books callbacks when a customer cannot talk.
A/B-testing suite for AI support agents: structured experiments (tone, logic, flows) against live traffic with control groups, statistical-significance testing and gradual rollout.
Dashboards and natural-language querying ('Ask AI') over support data: CSAT/deflection performance tracking, heatmaps, customer-journey visualization and knowledge-base performance.
Identifies customers' personal AI agents in live chat and voice, routes their requests through a dedicated agent channel, and lets the business control what those agents can do.
Analyzes support conversations to detect gaps in a company's help center and auto-generates draft articles, ranked by impact, with monthly updates.
Integrated testing suite (internally called 'Simulations') that validates AI-agent behavior across channels before production deployment.
Always-on monitoring and QA for AI and human agent interactions against custom quality criteria.