Voiceflow is a no-code platform for building and deploying AI chat and voice agents through a visual canvas, with LLM integration and knowledge base support.
Voiceflow is a no-code platform for building, testing, and deploying AI chat and voice agents. You design conversational flows on a visual canvas, connect them to a knowledge base and an LLM of your choice, and deploy the agent to your website, app, or phone channel. For product and support teams that want to ship an AI agent without relying on an engineering team to build and maintain the underlying infrastructure, Voiceflow provides the design and orchestration layer.
Where frameworks like LangChain or LangGraph give developers a code-first way to build agents, Voiceflow is built for non-technical builders who need to design, test, and iterate on the conversation experience without programming.
Voiceflow provides a canvas-based interface for designing how an AI agent should behave: what it says in different situations, how it handles user input, when it escalates to a human, and what it does when a step fails.
You build flows by placing and connecting blocks. A text block sends a message. A capture block collects user input. A condition block branches based on what the user said or what variables contain. An AI step sends the conversation context to an LLM and uses the response to continue the flow. A knowledge base step retrieves relevant content from your own documents and passes it to the LLM as context, which is Voiceflow’s version of RAG.
The result is a complete agent configuration that Voiceflow hosts and serves. Deployed agents can connect to your website via an embeddable widget, integrate into a customer support platform via API, or answer inbound phone calls through Twilio or Vonage.
Customer support teams use Voiceflow to build agents that handle tier-one queries automatically: answering FAQs, checking order status via API, troubleshooting common issues, and routing complex cases to a human agent. The visual canvas makes it practical for a support team lead to design and update the agent’s responses without waiting for a developer.
Product managers at SaaS companies use Voiceflow to prototype AI assistant features before committing to a full engineering implementation. A working agent on Voiceflow proves out the conversation design and user experience, and the backend API integration work follows once the concept is validated.
Agencies and consultants use Voiceflow to build and deliver AI agent projects for clients, with Voiceflow’s workspace and collaboration features letting multiple team members work on the same agent simultaneously.
Voiceflow hosts the agent infrastructure: conversation state, flow execution, LLM calls, and knowledge base retrieval all run on Voiceflow’s backend. You interact with this infrastructure through the canvas editor, and your users interact with it through the deployed chat widget or API.
When a user sends a message, Voiceflow evaluates the current state in the conversation flow, runs the appropriate block (which might involve calling an LLM, querying the knowledge base, or calling an external API), and sends the response back. State is maintained per conversation so the agent remembers context within a session.
The knowledge base works by indexing your documents into a vector store. When a knowledge base step runs during a conversation, Voiceflow retrieves the most relevant chunks and passes them to the LLM as context alongside the user’s message, a standard RAG pattern. You control how many chunks are retrieved and what prompt wraps them.
For voice deployments, Voiceflow integrates with telephony providers. Incoming calls are routed to your Voiceflow agent, the audio is transcribed, the agent processes the transcript and generates a response, and the response is converted to speech for the caller. Voiceflow manages the integration layer so you configure this from the same canvas rather than wiring together separate ASR, LLM, and TTS services yourself.
Customer support deflection. An agent trained on a product’s FAQ and support documentation handles the 60 to 70 percent of inbound support requests that have known answers. It escalates to a human agent when the query is outside its knowledge base or when the user asks to speak to a person. The support team sees fewer repetitive tickets; customers get instant answers at any hour.
E-commerce order and shipping assistant. An agent embedded in an online store handles order status lookups (via API call to the OMS), return initiation, product questions, and shipping policy queries. The knowledge base covers policies; API steps cover transactional data. The agent handles the full range of post-purchase queries without human involvement.
Internal HR or IT helpdesk. An internal-facing agent answers employee questions about benefits, PTO policies, IT access requests, and onboarding checklists. The knowledge base is the employee handbook and internal documentation. Escalation routes to the relevant team for requests that require human action.
Inbound voice qualification. An agent answers inbound sales calls, asks qualifying questions (company size, use case, budget range), records the answers to a CRM via API, and either schedules a follow-up call or routes hot leads to a live rep immediately. This covers the first-response layer for leads that come in outside business hours.
Yes. The Starter plan is free and lets you prototype agents with limited credits. Paid plans start at $60 per month for Pro, which includes one editor seat, 10,000 monthly credits, up to 20 agents, and access to GPT-4o and Claude. The Business plan at $150 per month adds 30,000 credits, unlimited agents, and advanced privacy controls. Additional editor seats cost $50 per month on paid plans. Enterprise pricing is custom and includes SSO, private cloud hosting, a dedicated account manager, and unlimited credits.
Sign up at voiceflow.com and start on the free plan. Create a new agent, open the canvas, and add a text block with a welcome message. Add a capture block to collect the user’s first question, connect it to a knowledge base step or an AI step, and run the agent in the built-in simulator to test how it responds. Once the conversation logic works, connect your knowledge base documents and deploy to the embeddable web widget. The Voiceflow documentation and community templates cover common patterns like FAQ agents, lead capture, and escalation flows.
Yes. Voiceflow has a free Starter plan for prototyping with limited credits and agents. Paid plans start at $60 per month for Pro (one editor seat, 10,000 monthly credits, up to 20 agents). The Business plan costs $150 per month for more credits and unlimited agents. Additional editor seats cost $50 per month on paid plans. Enterprise plans with custom pricing add SSO and private cloud hosting.
Voiceflow is designed for product managers, support team leads, and no-code builders who want to ship AI agents without writing backend code. Developers can extend agents with API steps and custom functions, but the core use case is non-technical teams designing, testing, and iterating on conversational experiences that a developer would otherwise need to build and maintain.
Yes. Voiceflow supports both chat and voice agent deployment. Voice agents connect to telephony providers including Twilio and Vonage, so you can deploy an agent that answers phone calls. Voice interactions consume credits faster than text, and some flow elements behave differently in voice mode. Most teams start with chat and add voice later once the core conversation logic is validated.
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