Your AI Vendor’s Vendor Has a Vendor.

Multi-vendor voice AI is five invoices, five failure modes, and state management you build yourself. One platform where AI runs inside the media engine eliminates the vendor chain.

2.7B

minutes and messages annually

2,000+

companies in production

< 1.2s

typical AI response latency

$0.16

per minute, AI processing

The Vendor Chain Problem

Five Vendors to Make One Voice Call

Five Invoices

Telephony, STT, LLM, TTS, and state vendors bill separately, scale differently, and maintain separate support queues.

Five Failure Modes

When one vendor throttles, the entire pipeline degrades and latency cascades through every component.

Five Escalation Paths

A dropped call sends you across five dashboards and incompatible log formats.

State Management Is Your Problem

No vendor owns call state. You build it with Redis, webhooks, retries, and reconciliation logic.

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()

Multi-Vendor Stack vs. Unified Platform

Multi-Vendor (You Build Everything)

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 (One Platform)

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

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.

Replace Five Vendors With One Platform

Build voice AI on a unified stack where AI runs inside the media engine and the control plane handles state and routing.