—— Production-Grade Voice AI ——

95% in the Demo. 67% in Production.

Multi-vendor voice AI creates failure modes that do not appear in development. One stack eliminates the vendor chain that breaks under load.

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2.7B

minutes processed annually

2.7B

minutes processed annually

2,000+

companies in production

2,000+

companies in production

< 1.2s

typical AI response latency

< 1.2s

typical AI response latency

internal network hops for AI

—— Why Demos Break in Production ——

Failure Modes That Only Appear at Scale

Scaling Mismatches Across Vendors

Your telephony provider has per-account concurrent call limits. Your speech-to-text provider has request rate limits. Your language model has token rate limits. Each scales differently. When one throttles, the entire pipeline degrades.

Scaling Mismatches Across Vendors

Race Conditions Under Load

Multiple webhooks for the same call arrive at your server. Both read state. Both mutate. Last write wins. Data loss. This does not happen with one concurrent call in development.

Race Conditions Under Load

Multiple webhooks for the same call arrive at your server. Both read state. Both mutate. Last write wins. Data loss. This does not happen with one concurrent call in development.

Cascading Latency

Speech-to-text delays push language model calls later, which push text-to-speech later, which overlap with the next conversation turn. Each hop compounds delay. Six vendors means six potential bottlenecks.

Cascading Latency

Stale Context After Interruption

The caller interrupts during AI generation. Your app receives the full response and sends it to text-to-speech. The caller hears an answer to a question they already corrected.

Stale Context After Interruption

The caller interrupts during AI generation. Your app receives the full response and sends it to text-to-speech. The caller hears an answer to a question they already corrected.

Build a Voice AI Agent

Python

TypeScript

Java

Ruby

PHP

Perl

C++

01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22

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()

Python

TypeScript

Java

Ruby

PHP

Perl

C++

01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22

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()

Python

TypeScript

Java

Ruby

PHP

Perl

C++

01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22

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 Pipeline vs. Single Stack

Multi-Vendor Pipeline

Each vendor scales differently under load

Distributed state creates race conditions

Network-dependent event delivery causes out-of-order processing

State desync between systems creates zombie calls

Network-delayed cancellation sends stale responses

Four separate dashboards, no correlated traces

SignalWire Single Stack

One system with uniform scaling characteristics

Platform-native sequential state processing

Events processed in order within each call context

Platform owns the full call lifecycle

In-process cancellation prevents stale context

One trace, one dashboard, every component visible

Failure Modes That Disappear

Failure Mode

Multi-Vendor Cause

Single-Stack Resolution

Scaling mismatch

Each vendor throttles at different thresholds

Uniform scaling, no cross-vendor bottlenecks

Scaling mismatch

Each vendor throttles at different thresholds

Uniform scaling, no cross-vendor bottlenecks

Race conditions

Distributed state across vendors

Sequential state processing within the platform

Race conditions

Distributed state across vendors

Sequential state processing within the platform

Out-of-order events

Network-dependent webhook delivery

In-process event ordering

Out-of-order events

Network-dependent webhook delivery

In-process event ordering

Cascading latency

Each network hop compounds delay

Zero internal network hops between components

Stale context

Network-delayed cancellation after interruption

In-process cancellation, zero round-trip

Zombie calls

State desync between systems

Platform owns the full call lifecycle

Zombie calls

State desync between systems

Platform owns the full call lifecycle

Error Recovery: Automatic, Classified, Transparent

Error Type

Fatal?

Platform Response

STT failure

Recovery phrase asks caller to repeat

STT failure

Recovery phrase asks caller to repeat

LLM timeout

Retry with fallback model

LLM timeout

Retry with fallback model

TTS failure

Fallback voice or text-based response

TTS failure

Fallback voice or text-based response

Tool handler timeout

Inform caller, retry or skip

Tool handler timeout

Inform caller, retry or skip

Tool handler error

Error-specific recovery phrase

Tool handler error

Error-specific recovery phrase

Network partition

Yes

Graceful hangup with state capture

Network partition

Yes

Graceful hangup with state capture

Authentication failure

Yes

Redirect to appropriate flow

Authentication failure

Yes

Redirect to appropriate flow

From Demo to Production

Build your agent

Define the agent in Python or YAML. Test locally with real phone calls.

Deploy to production

Ship the same code to production. The architecture does not change with scale.

Scale without rearchitecting

The system that handles one call handles ten thousand calls the same way. No vendor chain to coordinate.

Monitor with native observability

Per-component latency, barge-in analytics, and error classification are built in. No third-party APM required.

Recovery is automatic

Non-fatal errors trigger recovery phrases automatically. The caller may never notice. Fatal errors execute graceful shutdown with a hangup hook that captures final state for debugging.

Frequently asked questions

FAQ

What causes the demo-to-production gap?

In development, you test one call at a time on a fast network. In production, multiple concurrent calls hit vendors with different scaling limits, different failure modes, and different SLAs. The failure modes are structural, not bugs in your code.

How does a single stack prevent race conditions?

State lives inside the platform. The media engine holds it. Events are processed sequentially within each call's context. There are no concurrent webhook deliveries and no external state stores to race against.

What happens when an error occurs mid-call?

The platform classifies the error into one of 10 types. Non-fatal errors trigger automatic recovery (retry, fallback model, recovery phrase). Fatal errors execute a graceful shutdown and capture final state.

Can I bring existing agents onto the platform?

Yes. The Python agent framework and declarative YAML both support existing conversation logic. Your tool handlers receive authoritative context from the platform.

How many concurrent calls can the platform handle?

The infrastructure processes 2.7 billion minutes annually across 2,000+ companies. Built by the team behind FreeSWITCH, the open-source telephony engine powering carrier-grade deployments worldwide.

Try it now

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.

Trusted by 2,000+ Companies

Ship Voice AI That Works Like the Demo.

Build on a single stack where production behaves the same as development. No vendor chain. No surprises under load.

Start building

Dive into docs

Ship Voice AI That Works Like the Demo.

Build on a single stack where production behaves the same as development. No vendor chain. No surprises under load.

Start building

Dive into docs

Ship Voice AI That Works Like the Demo.

Build on a single stack where production behaves the same as development. No vendor chain. No surprises under load.

Start building

Dive into docs

Ship Voice AI That Works Like the Demo.

Build on a single stack where production behaves the same as development. No vendor chain. No surprises under load.

Start building

Dive into docs