SharkNinja built an AI agent that greets customers with a personalized espresso machine setup, guiding them through flushing and calibration while answering questions without interrupting the flow. The agent pulls from structured data and intelligently chunks PDFs to surface reliable instructions, recipes, and product registration.
So if I go ahead and scan this QR code, this is going to kickstart the unboxing agent.
Right.
And then as you can see, it's not a generic welcome at all. The agent already knows exactly the product that we're unboxing. The model number was passed through. And actually, this machine is a really great example of why an unboxing agent is so powerful. It's going to get you a great coffee, but there's a few very important steps that you need to do before that. Ah, okay. You've got to flush the machine. You've got to do a couple of calibration runs. You've got to find the right grind setting for your beans.
There's only one setting that counts. Strong. Yes, exactly.
And this is where guided setup comes in. So you click through all of these steps and it's going to tell you exactly what to do. And what's great is if you're ever stuck, you can also click to get more help. And on top of that, if you have any other questions about the product, you can just go ahead and type it in. So a lot of our customers might ask, what's the difference between the espresso strength options?
But you type it in and it doesn't take you out of the process of the agents bringing you through.
No, not at all. The context is going to persist. It's going to give you the question. But actually, you can scroll back up and go back into your guided setup journey.
I love that.
So it was a really simple idea. We want to meet the customer in those first moments. So, it's not just set up, answering questions. It's also going to give you, you know, the best recipes for an espresso macchiato. It's going to, you know, connect you to our social media or help you register your product.
Oh, registering is so important because I always forget. So, that it's part of the process. That's great. Great reminder. I'd love to see, how did you build that agent and that whole experience?
Let me take you a little bit behind the scenes. So this is our agent. We've built this using AgentScript. Mm. It was really new territory for us. You can see here on the left are our different subagents. This one is product greeting. You saw that in action just now. It's that personalized greeting. So the QR code actually passes the model number to the agent, and it's going to set the tone. A different product with a different model number will again send the right Product to the agent, so it's the same agent but a different experience. It's going to be scalable. Another sub-agent I wanted to show you is Product Assistant. You just saw that in action as well. This is what powers the guided setup and also those contextual questions. What's really great is that this is all powered by pre-structured data in Data 360. Also really great is that level of determinism that we have here. Really important for those guided setup steps. They have to happen in a very specific sequence, but also the flexibility to ask natural questions for the LLM to kick in. That flexibility is super important to us. Another thing I wanted to show you, flexibility of how we work in this agent has been really amazing. Right now you can see we're in Canvas view. This is the low-code visual way of building. But we also have the option to toggle to script. This is really good for our developers that want more programmatic and precise way of coding. It's the exact same agent, it's just a different view. When I first saw this, I was a little bit worried. All of our developers are trained in a different way, but actually there's so much training available, lots of powerful AI tools to help you not only transfer your existing agents but also build from scratch and troubleshoot. So something that we use is the inbuilt AI assistant. So it's really useful. For example, when we have an issue like the action isn't triggering or a variable is coming through wrong, we can ask the agent here and it's going to give you a really intelligent response and some tips on how to fix it. So that's been super valuable for our team.
That's amazing. So you guys have gone through the whole evolution of moving into the agent script and then working in in a new way.
Yeah.
One of the key things to the agents performing, and I like to think of as like a key thing to a good cup of coffee is the bean. The main ingredient for a good performing agent is data, grounded in good data. But at the same time, too much data can be overwhelming. And so I see your, like, your amazing PDFs like that, which my husband would love 'cause he'd read from, you know, top to front. How do you handle unstructured data with your agents without overwhelming it?
Mm-hmm. So this is actually where we use something called Intelligent Context. Here we ingest all of our product information, FAQs, instruction booklets, manuals. We do have a lot of product data still living in PDFs. Our CX partners are in the process of digitizing that. Obviously PDF's great in print, But they're in the process of making it great for AI consumption and also digital human consumption. You can see why. This is an example of one of our PDFs. There's so many images, diagrams, lots of information here. Historically, this would have been super hard for an AI to understand. If you imagine you just scrape the raw text from here. Right.
Without the pictures.
Yeah. It's going to lose all that context, the diagrams, the sequencing, the images. And what Intelligent Context actually does, it has an LLM run on the PDF and understand the PDF. And then it chunks the understanding and the context rather than chunking the raw text.
Okay.
And let me show you how that works.
I'd love to see that.
So if you remember, we asked the agent, what's the difference between espresso strength.
And we all agree that the only strength that matters is strong.
Exactly. So you can see here, this is the answer we got before in the agent as well. But I can click into the chunks that created this answer. And then I can also see the passages of where in the PDF this came from. If I open this up, it's not verbatim copy-paste of the PDF. It's that understanding. And that's—
So it's the agent's understanding of bringing in that data.
Exactly. And all of the context remains here. So it's like a really verified knowledge base of this PDF.
And if you saw something there, if you were reviewing it, maybe in your processes where you have a human in a loop is that they review these chunks. If they see something that has been interpreted a little off, they can tweak it.
Exactly. And that transparency is so important for a brand like Shark Ninja. So it's been super valuable.
