Canonical AI
Canonical AI's Voice AI Evaluation suite provides comprehensive insights into the performance of your voice AI agent, enabling you to monitor and optimize key metrics like signal-to-noise ratio, latency, interruptions, words per minute, and pitch to deliver superior voice AI experiences that engage users and drive successful interactions.
What is Canonical AI?
Introducing Canonical AI, a cutting-edge platform revolutionizing Voice AI analytics and evaluation. With features like Voice AI Evaluation, Call Path Analysis, and Voice AI Quality Metrics, Canonical AI offers comprehensive insights into your Voice AI agent's performance. Monitor key metrics like Signal-to-Noise Ratio, Voice AI Latency, Interruptions, Words per Minute, and Pitch to ensure optimal user experience. Easily identify and resolve issues with real-time monitoring, deep dive analysis, and daily voice metrics. Canonical AI provides tools for seamless evaluation, ensuring your Voice AI operates at its peak efficiency. Choose from flexible pricing plans designed for teams of all sizes and experience the power of AI-driven conversations. Improve your AI Voice Assistant Quality with Canonical AI. Copyright © 2024 Canonical AI, Inc. All rights reserved. Privacy Policy. Terms of Service.
Canonical AI Key Features?
Voice AI Evaluation: Easily find and fix issues with your Voice AI. Monitor SNR, pitch, latency, WPM, and interruptions in real-time to ensure optimal performance and user experience.
Call Path Analysis: Visualize call flows and classify outcomes, KPIs, and more! Identify drop-off points, track custom metrics, and analyze call performance for improved Voice AI quality.
Interactive Demo: Experience our Voice AI analytics through an interactive demo. Explore caller journeys, common conversation flows, and calls that meet specific criteria for a hands-on demonstration.
Pricing Plans: Choose from free, professional, or enterprise plans designed for teams of all sizes. Get started with flexible pricing options and up to unlimited call recording minutes for comprehensive monitoring.
Voice AI Quality Metrics: Monitor SNR, speech-to-text errors, latency, interruptions, WPM, and pitch for optimal Voice AI performance. Improve user experience, adaptability, and AI agent quality with detailed metrics.
Deep Dive Analysis: When your Voice AI calls fail, take a deep dive into detailed metrics to quickly identify and resolve issues. Analyze conversation flow, interruptions, and performance for efficient troubleshooting.
Evaluation Tools: Evaluate the impact of new features, bug fixes, or provider changes on your Voice AI. Ensure continuous optimization and quality control with daily voice metrics and performance evaluation.
Conversation Flow Visualization: Analyze the back-and-forth between AI and Human with a timeline visualization. Identify conversation dynamics, turn-taking patterns, and interruption frequencies for enhanced Voice AI interactions.
How to use Canonical AI?
1. Login and get started with an API key to improve your AI Voice Assistant Quality.
2. Choose from Free, Professional, or Enterprise pricing plans for teams of all sizes.
3. Monitor SNR, pitch, latency, WPM, and interruptions in real-time to ensure your Voice AI is performing well.
4. Analyze detailed metrics for every conversation to find and fix issues with your Voice AI.
5. Evaluate the impact of your latest changes on your Voice AI to ensure it continues to perform optimally.
6. Visualize call flows, classify outcomes, and identify drop-off points in your Voice AI calls.
7. Define your own custom metrics to track what matters most for your Voice AI Agent's performance.
8. Analyze conversation dynamics, turn-taking patterns, and the frequency and nature of interruptions for insights into your Voice AI interactions.
Frequently Asked Questions
What metrics does Canonical AI track for Voice AI quality?
How can I improve my AI Voice Assistant's quality with Canonical AI?
What pricing plans does Canonical AI offer for Voice AI monitoring?
Why is Voice AI Latency important in Voice AI interactions?
How does Canonical AI analyze Conversation Flow for Voice AI?
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