LPRC - CrimeScience – The Weekly Review – Episode 238 Ft. Beau Nutter

Episode Date: May 21, 2026

On this episode of the LPRC CrimeScience Podcast, LPRC Host Tiffany Frison welcomes new LPRC team member Beau Nutter, Research Scientist, for a conversation on the future of real-time intelligence in retail. Beau shares his background, his role at LPRC, and how the fusion of technology signals is helping organizations create smarter, faster, and more informed responses to today’s evolving retail challenges.

Hi everyone and welcome to crime science. In this podcast, we explore the science of crime and the practical application of this science for loss prevention and asset protection practitioners as well as other professionals. Hello everyone and welcome to the LPRC Crime Science Podcast. I am your host today. My name is Tiffany Frizen and I'm a research administrator here at the LPRC. and today I am joined by Bo Nutter and we're going to be talking about his first ever R2P

about the fusion platforms which we'll get into in a little bit here but first, Bo, since this is your first podcast with LPRC I wanted to do a little bit of introduction for you to our members who are listening now just in case they haven't met you. So Bo can you tell us a little bit about yourself in your background. Of course. So my name is Bo Nutter. I am from North Carolina. I attended the University of North Carolina, Wilmington, where I had a degree in criminology and sociology, and then a minor in psychology. And then I continued there to graduate school, where I got my

master's degree in criminology. And then my main focus while I was in graduate school for research was methodologies and just doing correct research, avoiding questionable research practices in criminology as it's been a bigger topic as of the last few years with a lot of literature that's not able to reproduce very well. So that's where I wrote my thesis on. And then after graduate school, I went into law enforcement where I got a position as NCIC specialist with the Wilmington Police Department. which NCIC is the National Crime Information Center. It's basically just a large database where all crime information is stored by the FBI,

and then it's divvied out to each state, and then further controlled by whatever the state bureau is. For me, it was the State Bureau of Investigation. And then that's further spread out into each local agency, whether county or city, And so basically my job was to maintain this information center and share information with our law enforcement agency as well as local law enforcement agencies around us, whether it's providing it to officers on scene or other officers that have come in contact with individuals or stolen property that we need or that we have reports out for. So I did that for quite some time. And then I found out about the job here. So I applied.

And now I am a research scientist here at LPRC. Awesome. Yeah. And we're very happy to have you. So I take it from your experience. You've worked extensively with law enforcement, obviously. And as you know here at the LPRC, we also do that. So does it feel kind of familiar, like working with the law enforcement?

I know it's in a job. different capacity kind of here at the LPRC, but is it something that kind of feels like familiar, I guess, to you? Yeah, the idea of loss prevention and the whole, all the technology behind it isn't as familiar, but things like working with CAD data, RMS data, or when I hear about incidents, arrests, all things like that, that's where I have a lot of familiarity with or a lot of the systems that are used with law enforcement. So that's more of my side. So I kind of think of it as like three spheres. There's

like the solution providers, the retailers, and the law enforcement. And so when we talk about them, I have a lot of knowledge when it comes to the law enforcement side and then a little bit when it comes to the retailers and solution providers, but my most, I guess, knowledgeable area would be that law enforcement and then wherever that kind of

overlaps into the other areas. Awesome. Yeah. No, that's great. And I think we kind of have that idea. I know when we were rebrand, I don't know how much, I won't go into this too much, but when we were rebranding the logo and everything, that was kind of taken into consideration to kind of how we position ourselves within those three realms and the community at large. So it's a very good, like, what I want to say, in intuition

that you have there for how we, how we are here at the LPRC. So let's get into your report. You talked a little bit about this, but can you tell us kind of what your focus has been here at the LPRC so far and like what areas you're like working in? Yes. So so far I've mostly focused on organized retail crime and real-time intelligence as those are going to be the two working groups that eventually take over. Shout out to those working groups. So a lot of the project-based work that I'm doing has been sort of along there. But I would say more heavily focused on the real-time intelligence. and along with that, doing stuff for the Innovate Action Team. So there I am taking either projects that they've done in the past or programs have done in the past,

as well as new ones and current ones that are being focused on, and turning that into actionable intelligence through reports, such as the one that I wrote for this one, and other future reports that I'll be writing. So really, I'm just taking what we've done and making it usable for our members. Gotcha. Awesome. And that is a great segue into the report that we're going to be talking about today. So the title, and I'll mention this again, but this report is available on our Knowledge Center.

So we'll be talking about Bo's first report, fusion platforms in retail, how integrated systems improve detection, intelligence, and real-time decision-making. So first off, could you just give us a brief overview of the report? and kind of what went on behind this report. Yes. So a brief overview is that we have these technologies called fusion platforms. That's our goal is to get more implemented into retailers as well as law enforcement because it's a way to combine all existing technologies on one screen. And I'll talk a way more in depth on that later.

But in this, we have a lot of experience using these fusion platforms ourselves and we have a lot of experience. some experience through law enforcement and retailers. But this report kind of takes all of our knowledge and knowledge that we've gained through others to give a description as well as guidance on how to use these future platforms, the best practices to use them. And then more importantly, considerations that retailers or law enforcement should have before using or implementing these solutions. Awesome. Yeah, I think that's a great description of a fusion platform. And I know that like we are currently using a couple different ones and testing them out as well. So I know like a lot of this, especially for what you're writing about here, was done before you got here.

Can you tell us kind of how the like data and everything and the trials were gathered in order to have this information? So all of the knowledge that I have about future platforms has either come from Josh or Reed who really have most of the information about these platforms or my own understanding through using it and experiencing it. I have a little bit of information that I've gotten through talking to NRTCCA members or retailers that have had experiences with this. But most of my knowledge about these fusion platforms comes from Josh and Reed and talking to them, working with them as well as just demoing the platforms myself to kind of get an understanding

of what they're capable of. So I know we've done a lot of work with seeing what works, what doesn't within these different platforms, what can be integrated because there are issues with integration. So that's really where my understanding comes from is just experiencing it, playing around with it, and then learning from others that have more knowledge about it. And you did throw out an acronym there that NRTC. Yes, that's National Real Time Crime Center Association. Perfect.

Awesome. Just in case anyone wasn't aware. Great. And then, so what are kind of some of the key findings that have been, like, brought out through this project? I would say the most important findings would be that it's, the executive considerations that you want to have when getting a fusion platform are going to be what your goal is with this platform as well as what all you can combine into this platform. So the first of those, what your goal is.

So if you're a retailer and you want to connect your store to law enforcement, which would mostly be the goal with the fusion platform, is you want to take all of your technologies, put them onto one screen, and then be able to not only yourself view all of your lost wretched technologies in one spot, you want to be able to share this information with law enforcement and an incident where a theft has occurred or a violent incident or anything along those lines. So you can give as much information as possible to law enforcement. So if that's your goal, then you want to make sure you're set up to be able to do that.

So to do that, you need to know what law enforcement has. So if law enforcement has one specific platform, then most likely you're going to want to get that same platform. So find out what your local law enforcement has for their platform. You get that same platform. And then you want also to have all of your existing technology be able to be integrated into your platform. There's a lot of considerations you need to take into account for implementing your own fusion platform. But I'd say if your goal is to work with law enforcement, which would be really any retailer's goal here, you need to know, you need to work with them to understand what it is that they're using

and how you're able to connect to them so that you can share your intelligence with them to speed up cases or to speed up dispatch times or really anything, any sort of benefit that you can get to the law enforcement. Okay, awesome. And I know, like you also mentioned in the report about ecosystems, which is kind of a little bit broader outside of the, just relations to law enforcement. So could you talk about, like, in this instance, what an ecosystem is and what we're referring to there? Okay. So I guess I would talk too much about what a fusion platform is capable of,

but to give kind of a little background to an ecosystem. Yeah. The fusion platform takes, it's really one pane of glass. So instead of looking at, you know, your. cameras, your sensors, your alarms, a GPS, a map, you know, body-worn cameras that may be an asset protection or security might have, that would take you eight different screens to look at. That would be almost impossible to track all at once. So if you can take all of those and put it onto one screen that tracks it all for you, it would simplify every job. And it also enables you

to see a map of your store so that you can see exactly where each thing is happening, either through GPS or through having your cameras mapped out so you can see exactly what camera you need. That's what that future platform is allowing within your store. So you can have an ecosystem within your store or whatever you're implementing this so that you can have every system that you have, every technology that you have all onto one overhead view where you can see everything that's happening. You can click which cameras you need at which time. And then you're also able to push all this information to law enforcement or outside to other

retailers. So now to create this greater ecosystem, let's say you're in a plaza area for us that would be public plaza. You have many retailers all right next to each other. Right. So if two retailers are experiencing any sort of theft, they'd want to be able to work together. So you have two stores side by side. The first one experiences an incident. Well now maybe that person is going to go to the next store over and then rob from them or have some sort of incident. It's with them. If you have a connected ecosystem, you're able to share this information. So let's say everybody's now on the same fusion platform. There's four or five stores. Everybody's on the same platform. One of them experiences some sort of theft incident. You have all of this information

on one screen and you're able to now share this information over to a neighboring retailer, to give them a heads up, a warning to let them know, hey, here's this person. They just stole from us, be in the lookout for them. If you see them, you know, call 911 or more than that, they can continue to track them. So maybe they run out of that first store and run past the cameras at the second store. Now we can watch them as they're going. So as long as everybody's connected within that ecosystem, it's sort of a shared information stream. So we can go beyond the first retailer to the second, third, fourth to track this person

Or have everybody have sort of a heads-up, a warning, or like an automated pop-up for a license plate that is known to be connected to somebody that's committing crimes in the area. Yeah, a red actor or something like that. Yeah, and then everything can be sent to law enforcement from that. So now we have five stores, and they all have the same shared information. So we know who's committing what, we know what they look like, we know what they're driving, we know where they are,

and everybody's connected to be able to share that information to each other. Awesome. Just out of curiosity, so you mentioned the map portion of the platforms, do you happen to know how broad that goes? Is it just an area or like a neighborhood? Or could that be, let's say if I'm a retailer and I have stores in like Cincinnati and L.A. or something, would I be able to see all of those sensors on one map? So it would work like, let's say a specific retailer had a SOC operator, which was the security operation center. Yeah. They had their one operator sitting at his office. He would have access to each store's own fusion platform.

So they have one, a broad platform that they're using across all of their stores, and then somebody that's able to monitor a certain amount of their own. stores. So I don't, I'm not sure, honestly, if it would be that he can look at one map and see everybody. Right. But he could definitely narrow down what would be, you know, eight different screens per store into, there's five stores that say, five stores that he has to monitor, and now he just has five little screens I guess to look at for each store. Gotcha. So he could see a map of each store, but I don't know necessarily if he could see one large map that encompasses is all stores.

All of them. Okay. Gotcha. Now, I do want to touch on some, like, limitations and things, because you do note about that as well here. So what are some of the limitations that Fusion platforms currently have, and, like, how do we kind of not combat them, but, like, counter them? So I kind of touched on this when talking about connected to law enforcement, but really the biggest issue or limitation with fusion platforms is what they're able to integrate as well as what they're able to connect to.

So now there are some platforms where this doesn't matter, where if you have this platform, you can automatically just connect everything in and push all this information to law enforcement. It doesn't really matter what they have or what you have because all you're doing is sharing a screen that shows all of your existing technologies. But others that have maybe mapping GPS, things like that, capabilities where you're putting it all on one screen, might not be able to integrate all technologies. So kind of a rule of thumb is that your platform provider is going to be able to connect to all of their technologies. So most of these platform creators also have their own cameras, their own sensors, alarms, GPS, things like that mapping.

So that's easily going to connect into its own platform. But when you go outside of that, that's where it gets a little iffy, where it's kind of hard to know without trial and error, what's able to connect to what platform, what's able to integrate. I know we've done a lot of work, Josh specifically, has done a lot of work to try to find out what connects to what and what brands are compatible with each other. But that's something that's just difficult to know.

Right. And then when I talk about connecting to law enforcement, that's another thing where if you have one fusion platform, you might not be able to share all of your information through yours to law enforcement's platform if you have different companies. Gotcha. So really you just need to focus on what works with what.

Otherwise, if you get a platform that you just can't really do much beyond monitoring your own store. Gotcha. And if your goal is to connect law enforcement, then that would be a major setback. Very good. Good. Very good to know.

All right. We are almost at time here, but I just want to circle back to kind of the key things that people can take away from this report and from our conversation today in regards to implementing fusion platforms and kind of what the future of that looks like. So I think the biggest takeaways here are know what technology you have and what you want to implement into your platform or even if you need one. If you don't have that much technology, maybe it's not worth it to you to get a fusion platform because you have three things that you're looking at.

But now if you have five, six, seven, eight things you want to put on the one screen, now you probably want to get a fusion platform to be able to combine that one pane of glass so you can see everything, just one screen and monitor it all at one time. Along with that, you want to know what specific platform. you have and what it's able to integrate. So can you integrate your technology? Do you have the same brand? Do you have the correct brand?

And then are you able to connect to law enforcement? And so those would be really the main things that I would focus on. And then beyond that, you're talking about where do we see this going or with research. So there's quite a bit with connecting for real-time intelligence. So one of the biggest things that we're doing now, I guess, is working with in our TCCA, which is the National Real Time Crime Center Association, as they are trying to connect to more retailers to be able to have their information directly pushed to a real-time crime center.

Gotcha. So that if something's happening, it can be instantly pushed and sent to a dispatcher, and then stuff can be handled much faster than it usually would be. So we have ongoing projects with them, try to get more retailers involved to show them how this technology works and how much faster it can be to get some sort of response of law enforcement. And then we're also working with other retailers in a separate project through our Innovate Action Team, where we're trying to get as many onboard as possible to create an ecosystem in the Butler Plaza area that we use to get everybody connected through fusion platforms or cameras or other technology that they might have.

So those are really the two main ways that we're expanding this research. Awesome. Well, thank you so much for this conversation today. And I think it was a good point. I'll just kind of circle back to, like, if you're a retailer and you only have three types of sensors or something, this may not be something that you want to spend too much time devoting to. But it can be very useful to those who have a lot of different technologies that want to bring them all together. Awesome. Well, thank you, Bo for being here today. I think we'll just go ahead and wrap up. I did want to say if anyone is interested, you mentioned the projects that are going on in Butler Plaza here in Gainesville and with the N-R-T-C-C-A to reach out to you potentially. Yes, reach out to me.

And Bo's email is... Boe.n at L-T-R-C.org. Yes, we just changed them all. So good job, Bo. All right, well, thank you. again, Bo, for being here today. And thank you to all of our listeners. If you're interested in looking at this report in more detail, you can find it on our Knowledge Center. And make sure to listen to the Crime Science Podcast on all listening platforms. Thank you all. Have a great day.

Thanks for listening to the Crime Science Podcast presented by the Loss Prevention Research Council. If you enjoyed today's episode, you can find more crime science episode. and valuable information at LPRsearch.org. The content provided in the Crime Science Podcast is for informational purposes only and is not a substitute for legal, financial, or other advice. Views expressed by guests of the Crime Science podcast are those of the authors and do not reflect the opinions or positions of the Loss Prevention Research Council.