Multiple agents.
One conversation.
Zero code.

Traditional voice AI uses a single prompt to handle every part of a phone conversation — greeting, qualifying, answering questions, booking appointments, and closing. This one-size-fits-all approach breaks down fast. The prompt grows bloated, the AI loses focus, and call quality drops.

Multi-agent voice AI takes a fundamentally different approach. Instead of one monolithic agent, Dilr Voice lets you build specialized agents — each with its own system prompt, LLM model, temperature, tools, and max tokens. A greeter agent opens the call with warmth and captures intent. A qualifier agent asks the right questions to score the lead. A knowledge agent pulls answers from your RAG-powered knowledge base. A customer care agent handles support queries with empathy. And an action agent books appointments, sends emails, and updates your CRM — all in the same call.

The result? Each agent stays focused on what it does best. The greeter doesn't hallucinate product details. The knowledge agent doesn't try to book appointments. Context flows seamlessly between agents — the qualifier knows what the greeter learned, the action agent knows what the qualifier scored. Every handoff is invisible to the caller.

And you're not limited to predefined agent types. Create custom agents for any use case — marketing, onboarding, collections, surveys, re-engagement. Each agent can use a different LLM model (GPT-4.1, GPT-4.1-nano, or any OpenAI model), different tools, and different conversation styles. Configure everything from the visual editor — no code, no deployments, no waiting on engineering.

This is why enterprises running thousands of outbound calls choose multi-agent over single-agent voice AI. Better lead qualification. Higher conversion rates. Shorter average handle time. And AI that sounds like it actually understands the conversation — because each part of it is handled by a specialist.