Voiceflow

What is Voiceflow?

Voiceflow is a conversation design platform used by over 100,000 people to design, prototype, and build Assistants — from customer support agents to personal automation tools. It supports Assistants of any size or complexity, offers a user-friendly interface accessible to newcomers in AI and automation, and is built for teams working collaboratively on advanced AI agents through a flexible developer setup.

Who created Voiceflow?

Voiceflow was started by a team led by Victor Algaze and Ciprian Nastase, who began building AI agents after growing frustrated with the chatbot tools available at the time. Their goal was to make scalable customer interaction through AI agents easier. Today, Voiceflow is used by more than 250,000 users and teams across many companies.

What is Voiceflow used for?

Voiceflow’s core use cases include:

• Automating customer support with AI agents, including L1 and L2 support tickets handled through curated knowledge bases and Zendesk integration.
• Building custom responses, interfaces, and integrations, including dynamic carousels that pull from Zendesk help center articles.
• Resolving complex user questions, even those involving code-first API queries.
• Reducing ticket costs while improving the customer experience.
• Personal automation — building AI agents to streamline your own tasks.
• Enterprise-scale agent management — product teams can securely build, test, launch, and manage conversational AI agents at scale, with reusable integrations and custom developer features.
• Training AI agents on your specific data and tasks, including multi-step tasks.
• Vendor flexibility — work with any AI vendor you choose, including within an Enterprise Cloud setup, and avoid vendor lock-in.
• Community and collaboration — connect with a global community of over 250,000 users, share agent functions, and collaboratively design, prototype, and launch conversational experiences.

Notable capabilities include building WhatsApp Assistants powered by GPT-3 and speeding up AI product team sprints while scaling to new use cases.