Canonical AI

Maker

📌

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!

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8mo ago

Contact Umair

@tom_shapland Mapping caller journeys and identifying drop off points is a game changer for improving agent performance. Well done.

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8mo ago

Jeff Chen

Redcar

@tom_shapland Hi Tom! We use a provider underneath the hood (like a twilio equivalent for voice) but would love to do this in our application layer. We usually have customer examples along with our ai examples. Does your product let us benchmark these against each other?

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8mo ago

Tom Shapland

Canonical AI

Maker

@thisisjeffchen yes! Our platform would help your customers see how their calls are supposed to be going compared to how they're actually going in the wild. It's an epiphany for Voice AI users when they first see what's happening in their calls.

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8mo ago

Kwindla Kramer

Daily.co

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. :-)

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8mo ago

Tom Shapland

Canonical AI

Maker

@kwindla thank you for your kind words! We've learned a lot about Voice AI from you and your team. It's been fun being a part of the community with you!

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8mo ago

Maxim Makatchev

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.

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8mo ago