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Canonical AI — Analytics for your voice AI agent
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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Replies
@tom_shapland Mapping caller journeys and identifying drop off points is a game changer for improving agent performance. Well done.
@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?
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.
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.
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. :-)
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!
Maker
@ninacali4 You flatter! That means a lot to me coming from you! Daily paved the way for the rest of us in Voice AI.
Congrats on the launch! What’s been the most surprising learning your customer has had with your analytics?
Maker
Thank you, Luke! We're excited to work with developers using 11labs new Voice AI agents. Some of the interesting problems include...
- The AI just stops responding. In one case, the root cause is with a third-party provider's API. The developer built in a backup provider to handle the error case.
- The caller hangs up before giving the AI a chance to solve the issue. When developers delay in the conversation design the fact that it's an AI, success rates go up.
- The AI calling from the wrong list. The caller's kept saying on one of the sad paths, "I've never heard of your company." Lead qualification is a fast growing use case of Voice AI. But you can only call back leads who have consented (i.e., through an online form) to be called. In this case, the Voice AI developer went to their customer and gave them the insight that they were not curating their lists correctly. A win for the voice ai dev!
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!
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!
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?
@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?
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.
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
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.
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?"
Maker
@ihuzaifashoukat Yes! For example, we can user points of frustration, like having to repeat a phone number. Would love to chat in depth about this with you!
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.
Maker
@maxipesfix Love what you did there with 'canonical' :). Thank you, Max! Looking forward to seeing what you do with the product.
Sad Path Analysis sounds like a fantastic feature! Understanding the unexpected directions calls take can provide key insights for improvement.
Maker
@adekemi_faronbi We hope so! We're trying to help all you who are build Voice AI. Thank you!
Maker
@adekemi_faronbi thank you! We're thinking of renaming the company to 'Happy Path'
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.
Amazing tool, everyone that is building AI voice solutions should definitely be using that
Maker
@rood_judeley_joseph thank you! Excited to see what you're cooking up in Voice AI!
- - 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!