Canonical AI
Analytics for Your Voice AI Agent
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Tom Shapland

Canonical AI - Analytics for your voice AI agent

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Canonical AI
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We help Voice AI developers improve their agents. We map caller journeys. We show you where and why callers are dropping off. We provide audio metrics (i.e., latency) and conversational metrics (i.e., query for cases where callers ask for a representative).

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Tom Shapland
Maker
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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!
Jeff Chen
@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?
Tom Shapland
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.
Kwindla Kramer
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. :-)
Tom Shapland
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!
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.
Adrian Cowham
Maker
@maxipesfix Thank you! 🙏
Tom Shapland
Maker
@maxipesfix Love what you did there with 'canonical' :). Thank you, Max! Looking forward to seeing what you do with the product.
Chris W
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.
Tom Shapland
Maker
@cwbuilds1 We're early in our journey with this product. At this point, all of our customers have Voice AI agents that have a clear objective (For example, the Voice AI is an after hours agent for a HVAC business). We'd love to work with more open-ended Voice AIs. Would you tell me more about what you're building?
Chris W
@tom_shapland That makes sense. We probably need to define some clearer objectives for users in our app anyway. Yeah, it's a way of practicing speaking foreign languages with AI. We don't use an end-to-end voice product, but we've cobbled together TTS, STT and an LLM. Would we be able to implement Canonical with our TTS, STT and LLM system?
Tom Shapland
Maker
@cwbuilds1 Neat! What a great use case for Voice AI! Yes, users just send us a link to an audio recording. We take the rest from there. It doesn't matter how the voice orchestration was done.
Chris W
@tom_shapland Awesome - will give it a go. Thanks and good luck.
Jordan Dearsley
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!
Tom Shapland
Maker
@jordan_dearsley Thank you, Jordan! We love your product and your team!
Varun Jain
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
Tom Shapland
Maker
@varun_jain12 Yes, we provide audio analytics as well as conversational analytics. Users can see time speaking, percent of the call that's silent, interruptions, etc.
Haseeb Awan
This sounds interesting. We're testing voicebots and QA and analytics is a big pain point. I'll test it out
Adrian Cowham
Maker
@haseeb Thank you! We're looking forward to seeing your calls come through. Let us know how we can help, don't be shy about reaching out.
Tom Shapland
Maker
@haseeb Thank you, Haseeb! Looking forward to hearing what you think!
Rood Judeley Joseph
Amazing tool, everyone that is building AI voice solutions should definitely be using that
Adrian Cowham
Maker
Tom Shapland
Maker
@rood_judeley_joseph thank you! Excited to see what you're cooking up in Voice AI!
Jannis Moore
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!
Tom Shapland
Maker
Thank you, Jannis! I admire your work in Voice AI! Nice seeing you here!
Eric Landau
Congrats on the launch! Looks like a very easy way to get started with Voice AI. Very cool!
Tom Shapland
Maker
Thanks, Eric!
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