Vapi

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Vapi is a developer-first platform for building voice AI agents that handle real phone and web conversations. Rather than shipping a finished chatbot, it provides the infrastructure and orchestration that sit between a phone system and the AI models, running the real-time loop that listens to a caller, decides what to do, and speaks back. The result is meant to feel like a natural conversation, with low latency and the ability to be interrupted mid-sentence. The platform's defining trait is its modular, model-agnostic design. Every assistant is assembled from three swappable pieces: a speech-to-text transcriber, a language model, and a text-to-speech voice, each chosen from providers like Deepgram, OpenAI, Anthropic, Google, and ElevenLabs. Because these layers are configurable, teams can tune latency, quality, cost, and fallback behavior, and replace any component as the model landscape shifts without rebuilding the agent. During a call the agent can call custom tools and functions, such as booking an appointment or looking up an account, fire webhooks on events, and draw on connected knowledge bases and CRMs. Vapi distinguishes between single-agent Assistants and Squads, where several specialized agents collaborate on one call and hand off context as the caller moves between tasks like scheduling, billing, and triage. A visual Flow Studio lets teams sketch conversation flows for quick prototypes, though more complex logic moves into the API. Everything is reachable through an API, SDKs, and a dashboard, and the platform includes evaluation and test-suite tooling to catch regressions before production. Built as an API-first system, Vapi is used for inbound customer support, lead qualification, appointment scheduling, and outbound sales and screening, and it scales to high call volumes for enterprises that have moved customer interactions onto voice agents. It offers enterprise controls including SOC 2, HIPAA, and PCI compliance, single sign-on, role-based access, and guardrails. Its focus is the infrastructure layer rather than packaged applications, which gives technical teams fine-grained control over reliability, compliance, and how the agent behaves, while making it a heavier lift for non-developers.