Voice AI Callers Do Not Hang Up On

No interruptions, no repeating information, no cold transfers. The architecture disappears. What remains is the experience.

< 1.2s

typical AI response latency

0ms

barge-in detection delay

100%

context preserved on transfer

2.7B

minutes processed annually

Architecture Problems Become Caller Problems

Slow responses make callers doubt they were heard

Two to four seconds of silence feels broken. In-stack AI delivers 800–1200ms typical response time so conversation feels natural.

Late barge-in makes the AI sound oblivious

Media-frame processing stops the response within the audio frame when a caller interrupts.

Lost context makes callers repeat themselves

Platform-native state travels with the call. The caller says it once.

Cold transfers destroy trust

Warm transfers carry identity, authentication state, and a conversation summary.

Build a Voice AI Agent

Python

TypeScript

Go

Java

Ruby

PHP

Perl

C++

C#

from signalwire import AgentBase
from signalwire.core.function_result import FunctionResult

class SupportAgent(AgentBase):
    def __init__(self):
        super().__init__(name="Support Agent", route="/support")
        self.prompt_add_section("Instructions", body="You are a customer support agent. "
            "Greet the caller and resolve their issue.")
        self.add_language("English", "en-US", "rime.spore:mistv2")

@AgentBase.tool(name="check_order")
    def check_order(self, order_id: str):
        """Check the status of a customer order.
        Args:
            order_id: The order ID to look up
        """
        return FunctionResult(f"Order {order_id}: shipped, ETA April 2nd")

agent = SupportAgent()
agent.run()

What Your Callers Experience

Bolt-On Architecture

Separate telephony, STT, TTS, and LLM providers, each with its own SDK.

Your glue code manages state across all four services.

Each vendor adds a separate queue, invoice, and escalation path.

Compliance and observability must be rebuilt across every vendor.

16 to 23 months before a production call.

SignalWire

Telephony, STT, TTS, and LLM in one platform.

AI runs inside the media stack, not outside it.

800–1200ms typical response latency.

One vendor, one invoice, one escalation path.

Production calls in four to six weeks.

State management is platform-native.

From Install to Production

Day 1: Install and build your first agent

Install signalwire-agents, define your agent class, add a prompt, and make your first phone call to a working AI agent.

Weeks 1–2: Connect your business logic

Add tool functions that call your backend: order lookups, appointment scheduling, account verification, and the tools that differentiate your product.

Weeks 2–3: Structure conversation flow

Define steps with scoped prompts and scoped tools. Each step handles one phase of the conversation with focused context.

Weeks 4–6: Go live

Deploy your agent as a standard HTTP microservice. Provision phone numbers, route calls, and move into production.

Weeks 3–4: Test and iterate

Test with real calls. Iterate on prompts and flows. Use the sandbox environment for rapid development cycles.

Sigmond Runs on SignalWire

A voice and video AI agent built on the SignalWire SDK, wired to a live knowledge base, able to see what you show him. Build the same thing on voice, phone, WhatsApp, or SIP, or an entire white-label platform with SignalWire as the network underneath.

Call recording

This call is recorded. Audio and transcripts are used to improve our AI and customer support experience, and aren't shared or sold to third parties.

Build voice AI that callers want to use.

Sub-second latency, instant barge-in, and context that never drops.