Vapi

Vapi bundles every assistant from three parts: speech to text, a language model, and text to speech. You choose each one from a catalog that spans OpenAI, Anthropic, Google, Deepgram, Gladia, and ElevenLabs. That means you can pair the fastest transcriber with the model you already trust and the voice that fits your brand, then swap any piece later.

Vapi handles conversational AI at speed: average response times stay under half a second for enterprise clients, which is what makes turn taking feel natural instead of robotic. The company reports sub-600ms response times on its core stack. In practice that gap is the difference between a caller waiting awkwardly and a conversation that flows.

The platform has two ways to build. A single assistant uses one system prompt with tools and structured outputs, which covers most support and booking jobs. Squads link several assistants together with context preserving transfers, so a triage agent can hand a call to a billing specialist without making the caller repeat themselves.

Agents can make and receive calls on any phone number, or run inside your product through the web SDK. Both paths use the same agent definition, so you build once and deploy to a hotline and to an in-app voice button without rewriting logic.

Vapi agents call your own APIs, databases, and back office systems through tools and webhooks, exposed through the same voice agent API that powers the rest of the platform. That is how an agent checks a live order status or writes a booking into your calendar instead of just talking about it.

Simulations let you test calls before a real customer hears them, and monitoring surfaces where quality drops across every call. You can review transcripts, publish versions, and roll back to an earlier one if a change makes things worse.