Canonical AI - Analytics for your voice AI agent

Hi PH community! I’m Tom Shapland, a cofounder of Canonical AI. We love working with Voice AI developers to help them improve their agents! Most Voice AI agent developers are manually listening to a subset of their calls. Or they’re only finding out about issues with their agent when their customers complain. We thought there had to be a better way to analyze Voice AI calls at scale – so we built Canonical AI! The Cambrian Explosion of Voice AIs LLMs have changed the paradigm for Voice AI. Compared to rule-based systems (i.e., Amazon Polly), LLM-based Voice AI agents understand the intent of the caller and can more often resolve the issue without escalation to a human agent. Moreover, with LLM-based Voice AI agents, developers can build a Voice AI agent more quickly, onboard customers quicker, and iterate on the product faster. Our customers’ Voice AI agents are doing amazing things! It’s so much fun to see the agents achieve the caller’s objective, even in the face of skepticism from the human caller. But the world of LLM-based Voice AI agents is still nascent. We’re seeing tooling emerge for testing Voice AI agents before they’re deployed. We’ve building the analytics platform for understanding, improving, and reporting on performance after they’re deployed. Key Features

• - Call Journey Maps: Identify where calls are failing in the caller journey so that fewer callers drop off.
• - Sad Path Analysis: Find the calls that have taken an unexpected direction so you can redesign the conversation or try other technical strategies.
• - Audio and Conversational Metrics: Easily surface problematic calls with audio metrics (e.g., calls with a high percentage of silence) and customer conversational metrics (e.g., calls where the caller has never heard of the Voice AI agent's company).

Next Step Check out our demo! Analyze calls from a car dealership Voice AI agent in our demo! Try out the platform with your own calls! Get an API key on our website here! If you're not comfortable with uploading calls programmatically (i.e., via our python example, our Vapi integration, or our Pipecat integration, then send us a drive folder of your audio recordings. Chat with us in the comments! We’d love to hear what you think! Please ask questions or comment below!

If you have conversational voice AI features in production, you have pain points that Canonical AI can help you fix. I've been bouncing ideas around with Tom and Adrian since the beginning [*] of the current era of voice AI. They've built interesting, valuable tools and have worked closely with many of the early pioneers deploying voice+LLM applications. It's definitely worth looking closely at what Canonical AI can do today, and worth following the company as they continue to build. [*] sometime last year. :-)

Super useful part of the layer of AI accountability. I often here from the customers "but AI makes mistakes..." A tool like this could be a great way to show a "canonical" certification to objectively address such a concern.

Cool idea. Are you mostly tailored to customer service or do you have customers who use Voice AI for other purposes? We're an AI language-learning app, and need some way of working out exactly where users are dropping off in conversations.

As the founder of a voice API infra company, I know how hard it is for our customers to deploy, debug, and test their voice agents. This is definitely a real problem. Tom & Adrian are an awesome team, and I'm looking forward to seeing how this product develops!

Can this be used to provide our users of the voice agent with post call analytics about the conversation? e.g. filler words, time speaking, etc

Congratulations on launching this much needed tool for Voice AI developers @tom_shapland. This tool looks like a great asset for any team looking to refine their conversational AI. Great job keep it up.

This sounds interesting. We're testing voicebots and QA and analytics is a big pain point. I'll test it out

Amazing tool, everyone that is building AI voice solutions should definitely be using that

Absolutely love the idea of integrated AI Voice analytics! I've been using Langfuse for a while, so it’s awesome to see fresh innovation in voice AI. Keep up the great work!

Congrats on the launch! Looks like a very easy way to get started with Voice AI. Very cool!

Sad Path Analysis sounds like a fantastic feature! Understanding the unexpected directions calls take can provide key insights for improvement.

Awesome idea, would appreciate a better understanding of how you built it

Huge congrats to the Canonical AI team on today's launch! I love how you're shedding light on the blind spots of voice AI interactions. Here's a curious question: Can your platform also provide insights on how to improve the success rate of resolving caller queries without escalating to a human rep, or is that a future roadmap item?"

This is a super cool product. We need to have more insight into what AI is doing behind the scenes. Having this sort of high level analytics is crucial for any business with voice AI agents.

A great idea well executed - highly usefulf or voice AI teams like us!

Congrats on the launch! What’s been the most surprising learning your customer has had with your analytics?

Great team. Tom is a leader in Voice AI.

Congratulations on the launch, Tom! Go YC alum!

This is awesome! The drivers of AI agent performance are often so opaque when running in production at scale. I think Canonical will be one of those things where, looking back, it will be hard to imagine that we deployed voice agents without the types of insights that Canonical provides.