XR Privacy + Analytics: How to Measure Without Being Creepy (Tony Bevilacqua, Cognitive3D)
Spatial Analytics for VR and AR | Dauntless XR Podcast
If you’re a business owner, you probably spend a lot of time staring at analytics: website traffic, Shopify performance, Instagram metrics, abandoned carts.
But what happens when customers leave screens and step into smart glasses and headsets?
In this episode of the Dauntless XR Podcast, we sit down with Tony Bevilacqua, Founder & CEO of Cognitive3D, to talk about what “analytics in 3D” actually looks like—and why spatial analytics is becoming a must-have as XR moves from novelty to real enterprise deployment.
Episode highlights
XR analytics isn’t just “web analytics, but in VR.” The real value comes from spatial signals: gaze, movement, hands, and interaction in 3D space.
Cognitive3D’s pivot moment: standard metrics weren’t enough—session replay and spatial visualization unlocked the “aha” for customers.
Enterprise sales lesson: don’t chase the biggest deal first. Prove value quickly with a pilot, then help the program expand.
Training ROI needs measurement early. If analytics is added at the end, you may never get the next version funded.
Privacy can’t be an afterthought. Consent, disclosure, and “do not track” patterns will matter more as wearables go mainstream.
Meet the guest: Tony Bevilacqua (Cognitive3D)
Tony has spent over a decade in immersive tech, with roots in mobile analytics before founding Cognitive3D (originally CognitiveVR) in 2015. Cognitive3D works with major enterprises to help teams measure what users actually do inside XR experiences—so product teams can improve usability, prove ROI, and scale deployments with confidence.
What does “analytics in 3D” actually measure?
Traditional analytics tends to focus on clicks, pageviews, and funnels. XR introduces a totally different set of signals—because user behavior is no longer confined to a rectangle.
In XR, teams can measure things like:
Gaze and attention (what users look at, for how long)
Spatial movement (where users go, how they navigate)
Object interactions (what they touch, grab, activate)
Hands/controllers (how they attempt tasks)
Time-to-complete + errors (especially important in training)
A key theme from Tony: metrics only matter if they map to outcomes. The “right” data depends on what you’re trying to prove—safety, competency, compliance, consumer attention, or something else.
The “aha” moment: spatial visualization and session replay
Tony shared a pivotal moment for Cognitive3D: early on, the team built “status quo” analytics—session time, navigation, basic events.
Developers liked the idea, but the value wasn’t differentiated enough.
Then the team built an early version of what became a spatial session replay tool (Tony describes it as a one-to-one after-action review of what users did with their eyes, hands, and movement). That visualization made XR analytics feel native to the medium—and helped customers immediately understand what was possible.
The takeaway: XR analytics becomes compelling when it helps you see the experience, not just count it.
Enterprise XR: prove ROI early—or the program dies
One of the most practical points in the episode: analytics is often added at the end of a project, but it needs to be added at the beginning.
If you’re building an XR prototype or pilot, measurement is what protects the next budget cycle. Without evidence of impact, many programs never get a “version two.”
Tony’s framing is especially relevant for training and operational use cases:
Completion isn’t enough (fast doesn’t matter if it’s unsafe)
Track accuracy, errors, and competency
Use spatial signals (including gaze) to support situational awareness and safer performance
XR privacy: measuring without crossing the line
As spatial computing expands, the privacy conversation gets real—fast.
Tony’s stance is clear: capturing raw video of someone’s home or private space is not something users will accept (and shouldn’t be normalized). Instead, teams need to focus on measuring interaction with the experience, and implement clear consent and data governance.
In the episode, Tony discusses:
Disclosure: what data is collected and why
Consent frameworks: opt-in/opt-out patterns
Data retention + usage boundaries
The need for “do not track” style signals in XR
What’s next: smart glasses, mixed reality, and new constraints
As AR and mixed reality become more common, analytics will evolve—but it will also be constrained by what platforms expose through APIs.
A practical implication: developers and analytics providers can only measure what the hardware and OS allow. That makes manufacturer choices—and privacy-by-design decisions—part of the product strategy.
FAQ: Spatial analytics, XR training ROI, and privacy
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Spatial analytics is the measurement of user behavior in 3D environments—like where users move, what they interact with, and what they pay attention to—so teams can improve the experience and prove outcomes.
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It depends on the goal, but common training metrics include:
task completion
time-to-complete
errors and retries
accuracy and compliance
situational awareness indicators (often supported by gaze/attention signals)
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No. Eye tracking can add powerful attention and situational awareness insights, but many XR analytics programs still get strong value from movement, interactions, timing, and error data.
How do you measure situational awareness in VR?
One approach is combining task performance data (accuracy, timing, errors) with attention signals (where users look during critical steps) to understand whether users are behaving safely and noticing key hazards.
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One approach is combining task performance data (accuracy, timing, errors) with attention signals (where users look during critical steps) to understand whether users are behaving safely and noticing key hazards.
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Start with consent and disclosure, minimize data collection to what you actually need, define retention policies, and avoid capturing sensitive environmental data unless it’s essential—and explicitly agreed to.
Want to nominate a guest (or be one)?
We’re always looking for operators, builders, researchers, and enterprise leaders working at the intersection of XR and AI.
To nominate a guest or pitch yourself, email hello@dauntlessxr.com.
Full Episode Transcript
If you are a business owner, odds are you spend a good part of your time looking at analytics, whether that is your website traffic, your Shopify performance, your Instagram metrics, or the dreaded abandoned cart notifications. But what happens when customers leave screens and go into devices like smart glasses and headsets?
Customer behavior is no longer confined to this rectangle, and we now need analytics. In 3D, what does that even look like? Is it possible? That's what we're talking about this week with Tony Bevilacqua, the founder, and CEO of Cognitive 3D. We wanted to have this conversation with Tony because Cognitive3D provides 50% of the Fortune 100 companies, including Walmart and Meta.
With spatial analytics and during our conversation we cover everything. Tony talks about the pivot that led to the founding of Cognitive, his secret to enterprise sales, the privacy concerns and tools that we need to know about with smart glasses and headsets becoming more prevalent. And personally. My favorite thing to talk about how you can future proof by combining artificial intelligence and spatial computing.
So whether you have an online business, you think you wanna have an online business. If you're building in XR, if you sell to Enterprise, or if you are just a data nerd, this episode of becoming Dauntless is your unfair advantage. So let's get into the interview.
Lori-Lee: So Tony, thank you for joining us today. Could you tell us about how you got into XR. 'cause you've been in the industry for a hot minute. There's not too many of us that have been around since, you know, the 2015, 2016 era. And you have,
Tony Bevilacqua: Yeah.
Lori-Lee: and your, and your journey.
Tony Bevilacqua: Yeah, absolutely. I have been in the space for over 10 years now. My first kind of, introduction to the immersive space was really back in 2014. I was spending time. Building a mobile analytics company,
and part of that was, you know, we had quite a few game developers as our customers. So I spent time at like GDC at E three when that was still a thing. And there was this guy named Palmer Lucky, running around, showing off. This thing called the DK two back then. I was building kind of a product that already existed in the market.
I was doing it with like, not a lot of funding and trying to catch up on features that already existed. And so for me, you know, I'm interested in building something that's innovative, that is, the next generation of, new capabilities, new features, that type of thing. And I saw this immersive technology.
I did a DK two demo. it was just substantially different. I realized that there was a real greenfield opportunity in the immersive technology space to be able to provide a higher level of visibility in terms of what people are doing and how they're interacting, inside these 3D environments. So I, I still worked at my last startup.
I bought a few dev kits. I got a couple developer friends involved, and we started playing around with it. I became a bit of an evangelist. Then, you know started to explore what it would mean to collect meaningful data, for the immersive technology space. And so we ended up founding cognitive VR back then, which is now cognitive 3D, back in late 2015.
Sofia: That's, uh, quite the entry point, uh, starting with the DK two. I think my first was the Google view. But it was the one where you could put a pixel in it and flip it down, and I don't even remember now what it's called.
Tony Bevilacqua: At Google, Google Cardboard, you know, and they, they went absolutely crazy for years where everybody was buying those branded cardboards and putting their phones in them and stuff
Sofia: yeah.
Tony Bevilacqua: the, the worst possible intro that you could give somebody to VR and
Sofia: And yet I'm still here.
Tony Bevilacqua: set our space back a decade, but yeah.
Sofia: From the analytics perspective, one of the most jarring things for me, being exposed to VR for the first time was how different the data is. So most companies are gonna track clicks, page views, conversion funnels, all these 2D metrics in that you would use on mobile and web, your tracking or cognitive 3D is tracking gaze patterns, spatial movements, object interactions.
How do you educate customers on why these metrics matter?
Tony Bevilacqua: Immersive technology offers so much capability and so much visibility into what people are doing and how they're interacting in space. And so for me it's about helping the customer understand that. We have an opportunity to have a unique value proposition that is unique to this particular platform that helps underpin why we are all here. If you're gonna spend an exceptional amount of money, resources, and time to build an initiative in immersive technology that we should. Provide meaningful insights that are exponentially more valuable than the status quo.
And so that's typically the entry point. And then it's really about unpacking what it means to measure and, and what it means to demonstrate value proposition from these different applications. .
Sofia: You have a memorable aha moment either for you and your team or maybe a customer where a piece of data unlocked a different understanding about the user experience and then maybe changed the actions they took coming out of it.
Tony Bevilacqua: Yeah. Okay. So had a meaningful moment as a company, and so I had all of the wrong assumptions, you know, kind of coming into cognitive 3D. I believe that as a minimum baseline, you know, we could just measure the way we do on mobile. We could capture a lot of the standard metrics, the taps session time, you know, where people navigated to in a particular application.
And we started to build that way because that's my background. That's where I came from. That's where I was spending time. And we quickly realized, we actually went out and did a hackathon in, in Whistler, and we had this concept of what. What if we could collect the full user experience of what somebody did and, and, and how they behaved. And we ended up doing a hackathon in Whistler and created our first rendition of a tool we called CE Explorer. And CE Explorer is a one-to-one after action review of what users did and how they behaved and how they moved through space, what they did with their eyes, what they did with their hands, all that type of stuff.
And up until that point in the mid, about mid 2016. We were building all of the status quo metrics, we were building all of those things. We registered over 500 developers. They were super excited about using this product 'cause they wanna measure their applications. But the value proposition just wasn't there.
Like, it was just a standard set of measures. It didn't offer anything unique or, you know, innately valuable to, to the customers. Well, we started showing off our hack project of like what we created in Whistler. It was really an aha moment for our customers that they could have exceptionally differentiated visualizations and capabilities to be able to understand how customers are interacting in these applications.
So that was really a pivot point for the entire company where we kind of sunset everything we did. We fired all of our customers on the old product and then moved everything over to spatial analytics. And the second thing that we saw in our customer base was because the industry wasn't taking off at that particular moment in 2016, we had to make a really tough decision, which is do we keep trying to build a freemium product, which, you know, is kind of more of like a vr you know, standard method in mobile.
You know, you, you start on freemium and then you pay for a package and, and so on and so on. One of the other differentiating things that we saw is that there. Big companies that had signed up for our free product. We saw like ExxonMobil in there and Accenture and, and a bunch of others. And we were like, what are these, what are they doing, you know, with our, what are, with our product?
Like what are they doing with this capability? Why are they spending time with us? And we realized very quickly that enterprise was a very uniquely valuable area for this particular space, especially when you apply measurement because you start getting into. simulation, um, where you can think about completion, compliance, competency, the efficacy of a particular application, consumer research.
You start thinking about like workflows, gaze patterns, how people move through space, that sort of thing. And even academic research where it was like there's so many starving students that are trying to do, you know master's degrees, PhDs in research, and they're using our product to be able to collect some of this data.
So we felt we had something at that particular moment.
Lori-Lee: You've got 50% of the Fortune 100 companies as customers, plus Walmart and Meta, which are, you know, huge, huge enterprises as you pointed out. For a small business selling to these, these massive customers.
What's something you learned about enterprise sales that you'd wish you'd known? Year one?
Tony Bevilacqua: one of the things that we've kind of learned as we've been working with customers is not necessarily go for the big deal or, you know, kind of the knock it outta the park the first time around. What I really want the opportunity to do is show a customer what our value proposition actually is. Give them an opportunity to see it in practice with their, with their product, their capability, even if it's a short term license. Three six. 12 months, you know, something in there so we can show them what that art of the possible is. Help them underpin their value proposition for whatever KPIs they need to collect the ROI that they need to move, and allow them to grow their program. One of the biggest risks that we have is that analytics is typically something that you add at the very end of a project. And my perspective is you have to end it, add it the very beginning, right? And I'll hear a customer tell me, they'll be like, well, you know, we'd only have X number of sessions.
We're testing it with a small group, you know, we, we'll add it on the next rendition of the application. I'm like, if you get the opportunity to have another version of the application. If you have the opportunity to keep working on this program, right, if you're gonna spend, you know, a million dollars on a prototype or something on those lines, you better have demonstratable, ROI, that makes sure you can actually justify the next program the next. of the work that you're doing. And so from our perspective, we've shifted our mentality from let's go after the biggest customers with the biggest programs to let's help enterprises enter the space and be successful. And sometimes that means just being the expert in the corner for them, helping them decide what the right data is. Helping them figure out like how to plug it into the app and collect it. And then also providing them some expertise because the other thing they get scared about is they'll look at the data set and they're like, this is terrifying. Like, there's so much data here. Who in my team is actually gonna look at this?
And it's like, well, the dashboards are meant to surface you. The insights that actually matter, right. But if you're still worried, we have multiple data scientists on our team. Cognitive psychologists can kind of unpack, these data science things for you and help you build that value proposition to make sure that there's a future for your program.
Sofia: When I'm hiring product managers, one of my questions is always like, talk me through the analytics of a specific project. And I'm always looking for how early in the cycle they defined their metrics and how early they started building it into their features.
Because it is set, it separates. The elite product managers from the from the lower level ones, because those are the ones that you don't need to tell to think about ROI because it shows that they're thinking about it if they're putting baked in analytics into their earliest stories on a, on a backlog.
When you're looking at these data sets, is there a specific data point that you always find that surprises your customers? Is it gaze pattern? Is it moving through 3D space?
Tony Bevilacqua: I find that every use case is a little bit different, and I think that the way to think about cognitive is more of a toolbox, right? In terms
of all the different types of data that you can collect, and you have to have a bit of a, a meaningful mapping of. How a particular data set or insight maps to, you know, a particular outcome you're looking for in your program.
If we're dealing with, you know, dangerous situations or, you know interactions were. The key value proposition that they're after is, reduction in errors loss of use of equipment people, loss of material, health and safety reduction, workers' comp, like those types of end goals.
Then I start to go down the path of like, okay, so how do we measure accuracy? Let's talk about, you know, measuring like spatial behaviors and specific actions that users can take. Are they doing accurately? Are they. Doing it within an appropriate amount of time. But then we'll go down the further path and saying things like, what if we leveraged eye tracking or gaze to be able to demonstrate great situational awareness and help reduce the safety aspects and be able to demonstrate to our user it's not enough to do the work. It's also about doing the work safely. If I'm talking consumer research, it's a totally different, uh, value proposition that I tracking that we just talked about for situational awareness. That's more about attention, that's more about branding. It's more about packaging design. Did they, did it stop them in their tracks when they're navigating on an aisle?
You know, where did they spend time in a store? Like those patterns, those data inputs can be used differently for every single different use case, uh, depending on what's required. And so part of what we do is helping map that.
Sofia: You alluded to this earlier with a lot of your customers, you're looking at VR training programs and you know as well as we do, there's a lot of hype around VR training. You know, you see things like 80% better retention or 4% faster completion, which to your earlier point, if it's not safer completion than it doesn't matter that it was faster.
Tony Bevilacqua: Yeah, exactly.
Sofia: But we also see data, I'm sure you do as well, that VR adoption is still well under 30 million active users globally. Are we in a hype cycle and we're, we're, we're swirling in it? Or is this really the future of workplace learning?
Tony Bevilacqua: , I'm gonna be, optimistic here. You know, like we have one of the largest VR deployments in the world. I won't say who, but I think you know who. From, from our perspective, like it provides a high quality, capability in terms of being able to retain knowledge, being able to attain knowledge on a distributed basis. Being able to assess without bias creates a fair and level playing field for, for a lot of folks. And, I think as we think about AI and kind of the, the evolution of the workforce as well as. Baby boomers and, you know, other types of folks retiring and lots of physically hard jobs that are highly technical that need to be replaced. This is a, a, a very fair and, and you know a highly aligned technology to be able to store, that mind share and train the next generation of folks. So, you know, be it you have a retention problem in your business or you just want to be able to, you know, train people in a very standard way. XR is definitely you know, a like, just fantastic vehicle to do so and produces higher level levels of retention than any other type of training material that we've seen other than standing with someone and showing them.
Be with your own hands.
Lori-Lee: Ever encountered people that find this kind of data collection creepy, because I look at
Tony Bevilacqua: Yeah.
Lori-Lee: you have, like, I look at it and I'm like, I wanna know. How have you dealt with it?
Or what do you, what do you think the, the outcome of, of that kind of reaction is gonna be?
Tony Bevilacqua: Yeah, I think it's super important to be, disclosed to your, your end users, like what it is you're doing and, and why you're doing it. It's also about framing as well. I mean, you know, if we're thinking about situational awareness, we're. We're trying to keep people safe. We wanna make sure you don't cut your arm off in a machine. And we can do, be, do that better if, you know, we understand if you're doing things that are potentially dangerous. There's different types of workforces that potentially would also have issues with this other than individuals Unions come to mind in terms of like. Protecting their, their workforce from, you know, potential assessment or over assessment of, of, of workers and performance. Um, so I definitely think that there's like realistic views that the enterprises themselves need to consider. Both a consent framework how data's used, how long data is kept what it's being kept for, what's the agreement between the worker and the business. At cognitive, there's a few things we do.
We've got something called the XR privacy framework. And it's a consent framework that the enterprises can use. And basically what it does is it discloses data that's going to be collected from something like a cognitive 3D, why it's going to be collected and then an opt-in or opt out. Now, the business certainly has the capacity to say if they opt out of data collection, they can't use the training. That's fair, fair game. But it also provides some visibility on the types of information that this particular modality is gonna collect. So, we didn't see anything in the market over the 10 years in building our company, so we ended up creating this standard that is basically a do not track signal, for x. And we, we respect that do not track signal. So if you created a browser function or a consent framework or something that is displayed inside the headset. And you send a do not track signal, we will pick that up and turn off tracking and it allows you to choose what types of data streams you might all wanna collect, right?
Generally, it's important that folks consider the privacy implications of collecting spatial data. I think in work it's. I think it can be better accepted than potentially collecting data from the public, uh, more broadly.
And so I think you have to tread with caution in, in both areas.
Lori-Lee: I think it's something we will hear and people will hopefully be thinking more about as. Wearables become more mainstream. And, Speaking of headsets you know, we are starting to see spatial computing start to pop up almost everywhere between AR glasses, AI assistance. We just saw some news drop about open AI with their AirPods style thing that's happening. How, how do you see analytics evolving?
Tony Bevilacqua: We've been thinking about augmented mixed reality since the very beginning. I think that just in the last few years, it's really become a, a, a true reality where we can actually produce some really interesting things. And we've also seen some really great like Medic West apps in kind of mixed reality mode where we've been able to kind of explore some of the ways that we think about that area. Just saying, circling back to privacy just for a moment. I think it's wholly unacceptable for both business and consumer to just capture video of like where a user is. You know, I don't think anybody is gonna find it acceptable that I capture your living room, you know, or, you know, a private space in your home or you know what it is that you're doing.
And so for us it's always about the interaction with the experience, in terms of how people are interacting in those environments. There may be other contextual things that you can do in augmented mixed reality where you might start thinking about geospatial context. So, developers could be thinking about like, where do valuable experiences happen? Where do we see, you know, different types of inputs how do we place things in the world? That sort of thing. And so there's definitely like context that you can capture and augment and mixed reality that is helpful for the developer to build a good experience. A good example is like instead of capturing someone's living room, we could white box. You know that there's a coffee table, you know, in the center of the room, you know, or something like that, and help the developer understand that in these types of scenarios. They're asking, you know, I impossible interaction or input from their customer. And so our goal, you know, generally as an analytics company for this space is to manage the line between helping developers build experiences that are gonna make them successful and make sure that the market actually happens, um, as well as the, the privacy implications that it has for the end user.
And, uh, try to create a balance between the two.
Sofia: We're seeing a lot more little ar assisted reality scenarios. Curious of your take on, on this, right, where the digital objects are, you know, being overlaid often it's more of a display than a true hologram.
In the physical space, but it might clip into a wall, right? It doesn't actually have the contextual awareness. Do the analytics you are trying to capture in those contexts change?
Tony Bevilacqua: We have limitations, right? In terms of like what we can do. And some of those limitations exist for a reason. You know, as an example on, on the meta quest for, for example, in their mixed reality mode, they don't give you like a view of the entire space.
Sofia: Great.
Tony Bevilacqua: or, being able to understand like what the room looks like. But they'll tell you there's a coffee table, a chair, a lamp, or other types of things that are contextually relevant to, to what you're doing. If the APIs don't exist on the platforms, that's also a hard line by the hardware that you are create, you're, you're purchasing and trusting in that manufacturer, the APIs that they extend, not only to the developers, but also to companies like Cognitive 3D. So we leverage what is. The capabilities are, we follow the terms of service, the privacy policy that these companies offer. And then we also have some of our own lines in terms of like what we won't do, you know, in terms of, um, you know, uh, making these things relevant, in an experience like that. You know, I think the insights are a little bit more limited than I would like in terms of being able to understand just a simple overlay. But, uh, you know, certainly measurable. Yeah.
Lori-Lee: I whenever we start thinking about things like this with the, the privacy angle and the what is socially acceptable and, and what's not I always think of Roomba and it's,
Sofia: I was thinking of
Lori-Lee: yeah.
Sofia: my.
Lori-Lee: 'Cause we had one,
Tony Bevilacqua: Oh my goodness.
Lori-Lee: when Roombas were new, right? And it was this like realization of, oh, now it's iRobot that owns Roomba.
Right? Now iRobot has the layout of one of the floors of my house. And it was, you know, not something you thought about when you clicked add to cart, you know? But then once you got it and you, you have the thing and you're, you're dealing with it, you're like, oh, where is this? Data going, what on earth are they gonna do with it? What if there's a data breach and now someone has the layout of my house? You know, at the time those were. Really big realizations and concerns now where we've become a little bit more numb to it. 'cause we have so many products that are tracking, spatial data.
.
Tony Bevilacqua: I think that you can design products with privacy in mind. And, you know, a good example would be like if you're going to, you know, anchor and experience the side of a home, you know, storing that spatial data and maybe an enclave that exists within the headset as opposed to pushing it to the cloud or, you know, something along those lines. So I think that, you know, part of this is like manufacturer responsibility and then also just like.
The market being, and I know it's hard for consumers to kind of consider some of these things, but, you know, choosing something that's not gonna mortgage your privacy.
Sofia: On device processing and storage is one of our favorite levers to pull as developers, to be honest, anytime we. Need to add a little bit more of a privacy layer, or we just feel that it's the right thing to do. Our challenge is the compute power necessary, um, can sometimes become a limiting factor with the headsets that we want to use versus need to use.
But agreed with you. I think. The cloud is good for many things. But we as people in the XR ecosystem, especially when we're considering privacy, should not overlook on device processing, on device storage because it can lend a lot more security to what we're trying to do while allowing us to deliver the experience that we want the customer to have.
Tony Bevilacqua: I think the people that work in this space as well as robotics probably have a more refined view on this particular problem set and the types of things that we need to be aware of and cautious of than pretty much any other area. So I'm hoping that, uh, you know we try to all stay honest about like, you know, how these things should be designed and that we don't get into a situation where, you know, uh, everything is, ends up in the cloud by, in the hands of who knows who. But, another good example would just be the one that I. Paid attention to recently. It's just like DNA data, like 23 and
Sofia: Yeah.
Tony Bevilacqua: all that kind of stuff. Like you, you sign up for a product, you, you know, use it and then you know, the data goes into the cloud and then the company experiences some sort of turbulence and then there's some questions about where that data might, might end up.
Sofia: One question I love asking people who come on here, Tony, so I'm gonna subject you to it as well, is what is the most memorable AR or VR experience you've ever tried and why was it so memorable?
Tony Bevilacqua: Oh man, there's so many. It's a, it's an app called Headspace XR. It's made by the people that build the primary Headspace app. I don't know if you've ever tried this application or not before, but it's beautiful. Nexus Studios, created, this application and, uh, you know, conceptualized this, this world of mindfulness and meditation and, um, all these different things and, and you can kind of like explore the world.
And, uh, it has all these different concepts of, you know. Trying to you know, help you kind of navigate what you're feeling and, and your emotions and all these different types of things. And, uh, it, it never got a massive audience just in terms of like installs and, and what I've seen so far. But, um, it's a, it's a really beautiful application and, and deserves some attention as well as some accolades in terms of like what they created.
Sofia: I think you've motivated me to go try it because I've seen it before, but I have not tried it myself.
Tony Bevilacqua: It's worthy of a run through, you know, give it a, give
Sofia: Okay.
Tony Bevilacqua: you know?
I think it's, it's interesting
Sofia: Awesome. Well, thank you for joining us today and for sharing everything you learned.
If, uh, listeners want to hear more about what you do and what you're up to, where can they connect with you?
You can hit me up on LinkedIn. Also email tony@cognitivethreed.com. Couple things I'll mention, cognitive 3D is totally free for developers, so if you're interested in understanding like where you stand from an application quality perspective, we've got these really great benchmarks that would allow you to understand your application and and dig in.
And folks for that are interested in the privacy perspective. Please check out the XR privacy framework. That's XR privacy framework.org. It is a standard, it's open source. It's MIT. And so if you wanna contribute to it, if you want to use it, if you wanna respect it, like use it in your own data collection scheme in terms of, you know, respecting its signals, definitely check it out.
'cause that's something that we, uh, we, we, we do. But we're, we're, we're a group of one, unfortunately, right now, that's a great resource for the developer community. We'll only link to it in the note. Appreciate it. Thank you very much for having me. Yeah, thanks for coming on, and we'll be sure to link all of that in the show notes for anyone who's watching or listening.
We can't wait to talk more and we'll see you all next time.
