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What is Phonic?

Phonic is an AI voice infrastructure and conversational automation platform that enables developers and enterprises to create, evaluate, and deploy intelligent voice agents. The platform is built around Phonic-1, a native speech-to-speech model designed to conduct natural conversations while executing complex business tasks. Unlike traditional voice agent systems that combine separate speech recognition, language models, and text-to-speech engines, Phonic processes incoming audio directly and generates spoken responses through an integrated architecture. Its audio understanding capabilities recognize tone, hesitation, speaking pace, and other conversational characteristics to help agents respond appropriately to callers. The model supports full-duplex conversations, natural interruptions, accurate turn-taking, and multilingual communication across more than 50 languages. Phonic-1 delivers responses in approximately 600 milliseconds and can execute tool calls while users are still speaking to reduce delays during information retrieval and task completion. Its reasoning architecture uses frontier AI models as supervisory agents to support complex decision-making and interactions with external business systems. Developers can configure agents to retrieve customer information, update records, schedule appointments, process requests, transfer calls, and trigger automated workflows through integrated tools. The platform provides REST APIs, Python and TypeScript SDKs, command-line tools, a no-code agent builder, WebSocket connectivity, LiveKit integration, webhooks, and Model Context Protocol support. Monitoring and evaluation capabilities provide visibility into conversations, response latency, tool execution, and agent performance to support ongoing optimization.

What is Modulate Velma?

Velma is a cutting-edge AI model developed by Modulate, operating within an extensive voice intelligence framework that interprets conversations directly from audio input instead of relying on text transcriptions. Unlike traditional approaches that convert spoken language into text for analysis by language models, Velma utilizes an Ensemble Listening Model (ELM) characterized by a distinctive architecture that can simultaneously process various dimensions of voice, including tone, emotion, pacing, intent, and behavioral signals. This sophisticated ability allows it to capture the full essence of a conversation, transcending mere words to recognize subtle cues such as stress, deceit, sarcasm, or escalation as they unfold. Velma accomplishes this feat by integrating numerous specialized detectors, each focused on particular aspects of speech, such as emotional context, inappropriate behaviors, or indications of synthetic voices, and then consolidating these signals to extract deeper insights regarding the conversational dynamics. As a result, it enables a more profound understanding of interactions in real time, significantly improving the potential for effective communication analysis and fostering better engagement. Its unique design positions Velma as a leader in the realm of voice intelligence, pushing the boundaries of how we perceive and interact with spoken language.

Media

Media

Integrations Supported

Phonic-1

Integrations Supported

Five9
GENESYS
Microsoft Teams
Slack
Zendesk
Zoom

API Availability

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

$0.25 per hour
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Online Training

Company Facts

Organization Name

Phonic.ai

Company Location

United States

Company Website

phonic.ai

Company Facts

Organization Name

Modulate

Date Founded

2019

Company Location

United States

Company Website

www.modulate.ai/velma

Categories and Features

AI Voice Agents

Not specified

Categories and Features

AI Models

Not specified

AI Voice Agents

Not specified

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