Building AI Risk Mitigation with Ina Jovicic Enough CEO

honestly at the same time exciting and scary just because I feel like it's going to very change the things that we're focused on right now. It's always like like we're testing different things and we're like okay like the moment it's out there it's going to be whole new set of challenges and things to be conscious of and worried about. But at the same time like I really want to see it at use and see how we can even improve it and optimize it better and that's only going to be possible with more and more people using it. >> Hi my name is Demetri Bonichi and I'm a content creator, agency owner and AI enthusiast. You're listening to the AI Agents podcast brought to you by Jot Form and featuring our very own CEO and founder Idkin Tank. This is the show where artificial intelligence meets

innovation, productivity, and the tools shaping the future of work. Enjoy the show. Hello and welcome back to another episode of the AI Agents podcast. In this one, we have Ena, the founder and CEO of Enough. How you doing today? Today, Ena? >> Hello. Hello. Thank you for having me. I'm doing great. How about you? >> I'm doing awesome. Uh you're calling in today from the UK, right? >> Yes, from London. >> Nice. From London. Well, from across the pond. We'd love to learn a little bit more about um what you got going on over there. Obviously, how you got into um AI. I know in the prescript you had that you had a pretty interesting personal story. If you want to kick it off maybe with that or a different angle, I don't know. But yeah, just feel free to kind of tell me how

you got a little bit into or not a little bit, how you got into AI a lot of bit at this point. >> Yeah, 100%. Um I always like to start with the story because it kind of explains why we care about the space and why are we building in the space which is um I feel like in the context of AI important why we've chose exactly this narrative and the story kind of starts four four years ago when I was um finishing my masters in entrepre entrepreneurship at UCL in London and one night I went for a dinner with my friend and when I was returning from from that dinner through safe area of London around 10:00 p.m. I got surrounded and attacked by four guys that were trying to take my things. And now for the context, I'm a Bosnian woman. So naturally,

my first [snorts] response was like straight up like, "Well, you're not going to be taking anything from me. Like, forget it." Like I immediately immediately I thought is like, "Oh, like they think I'm a good target. Well, let me show them." But this is obviously like very much adrenaline speaking rather than anything. Uh, and it's definitely [clears throat] not the way the best way to think and handle that situation. But that's that's also going to link to the why we're building what we're building as well, like from the adrenaline point of like decision- making in that situation. But essentially, I then ended up getting dragged in the middle of the street while I was fighting for for my things that were trying to take and uh ultimately they I mean for against one they took everything and I was left uh with scrunch uh scratch

knees as souvenirs. Um, but the the important part here is when the police got there, they told me three core things. And one of them was that I was actually very very lucky because I could have apparently been easily stabbed just by the fact that I was fighting back, which was again a very like natural response in the moment. And they also told me that this is super normal, that this happens all the time, and this is their case that I don't even know which number they said that is only happening that night, right? And that that was like sort of the comments that already got me thinking like why is this so normalized? Um but further further from the then the conversation with the police when I was talking about this and sharing what happened with my friends with my female friends specifically the

part that really like woke up something in me is that every single time I would share the story to another friend she would have a story to share back very similar often worse. And that's really what got me to like look into the space because I was like >> how is it possible that this is a problem that is >> happening across all the geographies like every woman relates to and has become so normalized like why could have we never built anything to really solve it like what was wrong with all the other solutions. Um, so that's what got me like to really dig deeper and look in like okay like what has been the issue that this couldn't be solved. And what I found is that we've always relied on humans to be able to do react or do anything in these s sort

of situations like from using a pepper spray from pressing a hell button or your keychain from just like calling calling help in that moment or not letting the adrenaline like in my case affect you and staying calm and doing what's like what's going to give you essentially more safety rather than the opposite or like you know not freeze and so on. That is all super unrealistic for humans to do in that situation. And then when I look deeper, I realize that now for the first time ever, we can replace the human element entirely with AI. So essentially for me, the almost like the intersection of like how can we use AI to solve this old and forever problem and like use it for really good and actually add something that the humans can't do was the the part that fascinated me entirely about the space.

And that's why I went to the personal safety um with the intersection of like AI. >> Yeah. No, that that's uh one crazy story. Glad glad that went um as um okay as it could have cuz um >> I've heard crazy stories about that in London and I you know it's funny when we we had the call originally like you you you said to me that this had occurred and I was like I had heard the stabbings were happening in London so I'm glad that >> didn't occur cuz I know it's bad. Um but secondly like obviously AI you're saying that it can do the things that humans can't reasonably do in that context. >> One uh why is that to how are you doing it? >> Yeah. Yeah. So to tell you a little bit of context what the product is and how we're

actually including the AI. I have to first mention that we are building essentially a small tiny wearable. We're calling it the mini AI bodyguard, which is essentially just a fancy word of calling um a personal safety like safety companion in form of wearable. And it's about this size, so like I would say half a half a half an iPhone. And you easily clip it on your jacket or coat the moment you start walking. It's really important to mention that we're not a daily wearable. to never aspire to be a daily wearable, but it's something that you have always with you like AirPods and you only use it in situations of when you feel like it's it's needed or necessary. So, let's say that you are taking the last uh metro home, you're taking the last bus, and then you have that 15 minute, 10-minute walk

that you just don't feel really like at ease. So, that's the moment when you would clip it on and would be like sort of there to watch over. Um, and now the core superpower that the wearable has is it's our AI threat detection model. So we're essentially using multimodel AI, so audio and video to be able to detect danger. And the moment that we detect danger, we can autonomously deploy protective features based on the situation. We can deescalate it. We can escalate it even all the way to the police if that's necessary. and all while collecting evidence as part of it, which is the part that usually is missing. Like in my case, it happened in front of an embassy. So there was plenty of CCTV. And when I was trying when I was asking if we can get the footage or anything, they told

me like, well, it's it's impossible because like first of all, like we are only doing that for cases like stabbing and murder. And we don't even have the infrastructure to receive receive the footage in a time frame that would make sense for the case. Uh so entirely useless for the most like CCTV covered city in the world, right? >> So it's ridiculous, right? Um but that's why we knew that the evidence element is huge for our product. And then obviously like having that capability to provide that data in real time as well to the police. So let's say that our badge >> raises like now in milliseconds like hey this is something this is what happening. This is where it is this is who it's happening to and this is um the live stream. So they can see when they're going into the situation already

what's like who they're after, what's happening, but also they have that piece of evidence already like provided which is super valuable for these cases. Um so yeah, essentially our AI handles from detecting the threats to then deciding how to act on it and then ultimately like getting you the best help possible because every single case doesn't mean that it has to go to the police. Sometimes it's like you know much smaller cases or like it's a false alarm false alarm. So that's why we don't have to like it's never always the case. >> Yeah, that's that is wild. The most CCTV covered city said I can't really use it for something that I feel like would be the reason. Um, okay. Well, um, talking a little bit more about your product then, right? You said it's a little wearable, not an everyday sort of not

like your your AirPods, your, um, Apple Watch, but like how does it, um, from an AI standpoint actually detect uh, warnings and and, um, potential threats? >> Yeah. So, what we're doing is we're separating in two parts mainly. Well, let's say three parts, but two that I want to talk to you about first. So, audio, we're looking, we're constantly listening. when you have that when you clip that badge on and you activate it for that 10-minute walk, we're looking for any audio cues that might indicate that you're in danger. Because when we did our research, often times, like what like a typical case scenario that happens is they jump you with a with a knife in a UK case. I guess in a US case that would be a gun and they tell you, "Hey, do not move, do not scream, do not shout, give

me your things right now." >> And in that situation, you cannot do anything. Well, you shouldn't do anything. I don't want to say cannot, but like you would put yourself in more danger. So you are exactly >> so you are in a situation where you can't even notify anyone that's happening >> the that specific audio would automatically get picked up by our patch and it would know like okay there's something wrong and that we're already in that case raising the alert in the back in the back end and based on what's happening deciding like okay are we going to play this dees the escalation message or are we going to raise this straight away to the police. So this is what we're doing with the audio part because audio sometimes not everything is visible on the video and that's in the audio part we're looking

at factors like bunch of different factors that then come together. So from emotional analysis to sentient analysis to keyword spotting to like understanding the logic behind the sentence because there's a difference when someone says like hey like give me your wallet and someone like hey do you mind borrowing me like you know this card and no no like it's very we have to really always like look specifically how the sentence is said the the volume the pitch um for it to like give it almost like a ranking in our back end. So that's how we how we look at the audio part. And then on the other side of um we're looking always at the video as well because we're collecting evidence your entire walk that is spotting for any sort of patterns that would be dangerous. So from object uh analysis like if you

spot a gun, if you spot a knife, but also like patterns of behavior that could appear threatening like you have someone running towards you in an aggressive way. that is something that we're looking at and that is one of piece of the puzzles that is then provided together for the AI agent to make a decision what's happening if that makes sense for the context >> what does it do like when it determines that it's a a risky situation like what is the like >> thing itself >> so the moment we're we the agent decides that this is a potential threat what we do is that we in the milliseconds we push it to our alarm receiving center and our alarm receiving center is real humans >> and They essentially receive the threats afterwards and they double check with a human element >> if the AI

agent was right because in our case we're looking at a very sensitive >> topic and we cannot be wrong and that also helps us improve the model right so that's why it goes to alarm receiving center that then in case it double validates it automatically is pushing it then to the police because they're the alarm receiving centers for example here in UK they're all plugged in into all the systems security systems And the police traditionally like for this has been done for like years and years. So like for example every building that you see behind me like all of them have the what do they call it here bulgary alarms when someone would break in it doesn't go straight away to police. It goes to alarm receiving center. >> They have a look at the footage and then if they decide like okay this is

real like someone is stealing things then they push it to the police that is closest because they're already tapped into the network. So that would be in our case we have the badge on the person something goes wrong it raises the alarm to the alarm receiving center and alarm receiving centers are the people that do the the work that the police is only responding. So they would be like okay she's here and here the closest police officers that we know are three streets away so we're contacting that police department and not like I don't know police department of sorry in the UK context. Um so that's how it's them escalated in the bad cases of course. >> Gotcha. Okay, that makes sense cuz I always I was like wondering I'm like I don't know how that would really go if it was like an auto

police thing but that that makes more sense um in that manner. Um >> it's more like as well to like to add on to that because the more important powerful part of AI is the fact that you don't have to manually press anything like the moment you had that knife in you and you're like hey do not scream do not shout like you want to shout you need to shout but you shouldn't in that situation. So instead of you pressing the button and we kept the buttons there still because psychologically it just feels good that if you have the chance you can hold tight onto the buttons but the idea is that the badge like almost by its own presses its internal button to then push it to the alarm saving center. >> Okay. And um when you were kind of getting into this right

obviously like you had your story and everything. How did you necessarily go from one to the other like the you know like we need to do something about this and then like the R&D of it like how how did that whole process kind of go about? >> I think in my context it was very lucky with like where I was at that point in my life. So I just did I was just in the middle of that masters in entrepreneurship which is like a one-year crash course of understanding how to develop ideas and build solutions that actually people need. So really understanding the problems and how do we have that like problem solution fit and also understanding how to then if you have a solution that you believe is going to work how to validate the market how to validate you know the hypothesis that

you have to have sort of answering before you go fullon to raise investment and go forward. So when that happened I essentially when my story happened I used the entire year to do super deep market analysis and everything. I talked to so many users. I talked to um people using other solutions of the like buttons, why it doesn't work, what would they need like so much data around the personal safety. Then I al ultimately ended up writing a dissertation on it which gave me like a whole 360 analysis of everything in the space on like how we can how why does this product solve the unsolvable before and then when I had all that research that was more like okay we're building this let's do it and then the second part was more like okay now we have a tangible product so now we have

prototypes let's test the things that we can now test with having the physical product so we would run so many focus groups when we would give women the process science be like okay now like you know attach it to your clothing as you think it should be attached and we would watch them like you know figure out how what's the magnet attachment the clip attachment and we for example saw details that we never even thought about like a stupid example of for example the the magnets right like we have the magnetic attachment when you separate the magnet from it and that's how you clip it on and one of the girls was like hey guys but like I always wear like really long nails I would be worried that I'm going to break them every time I separate the magnets And obviously our core user

target, user persona is that female between like 20 30, right? So we're like, okay, yeah, we never thought about it, but that's important that we make it like an easy hook so we can separate it >> and not risk it. Tiny thing, but for a you from a user perspective, very important part of like the whole user flow. >> Yeah. >> Um, and same with like understanding why does having a camera inside of the badge matters that much to them. Like what is it about the psychological reason of having like the evidence? And then we got to this whole like perspective that essentially oftenimes when these things happen to women even even if it's like a a super creepy dodgey person looking at youly when they're retelling the story sometimes they start questioning themselves if it was really that bad and that's where the evidence

was sort of part of them like okay there's someone else that can prove that this is what happened and they can see like how bad it was or like how bad the situation was. Even the the very bad cases, sometimes you can't even explain to other people like how bad the situation was unless you have that evidence and you can never be able in that situation also to pull out your phone and start filming because that's probably going to get you even in more trouble. >> Yeah. So, exactly that part of like understanding the importance of evidence was really crucial or like um a lot of women probably relate to this, but a lot of them phone someone when they're walking by themselves because just feeling that there's someone hearing >> it makes you feel better, but like statistically like that puts you in more

danger again because you're exposing your iPhone and if there's something they're coming for it's the iPhone, right? So this is all the elements that we really really needed to break down because like the problem of lack of safety is so complex that we needed to understand like what are the layers and how can we answer every single layer with a different feature to build a product that really makes a difference in this space. >> Yeah. No abs. Absolutely. And do you feel like um the product is uh well a couple things you know seems obvious like what the ethos of your company is like just the the public safety there for um people who are in a situation like you but do you feel like the during the R&D process um anything maybe in your mind changed of like an an initial vision of what

the company could be like? Um like was there any changes due to that whole process you went through? I think from the beginning what changed the most is the importance and the role that the AI plays within the product. I feel like at the beginning it was more the wearable first AI second. It was almost like the churning top like it's a cool product and like with AI it's great. Now it feels like it's the AI inside of the our AI agent. It is the superpower and the wearable is the enabler. So now we look at the hardware as like pure enabler of the AI rather than the other way around. And I think with this also mindset shift it really f like allowed us as well to focus on like how can we make sure that that is the aspect of the solution that

is the strongest. Um so yeah that I would say it's but it's also like been affected with the timing and the industry changing like there's been so much things happening in the AR world that it was like kind of natural that we were going into that direction more and more and more with all the capabilities you can have and there's so many other like pathways that we can take with the within the space like from simple things as like when something happens and you're on the phone with the police often times they're just like transcribing what you're saying and it's a very manual process on the police side as well that they're like okay where are you what happened can you tell us like how you look brown hair and all that and it's super manual so even all that information like that's something in

the future that definitely is just going to improve the entire system so it's not just that we're innovating from the user perspective but I feel like that also the industry like stakeholders essentially everyone is moving with the AI further and optimizing everything which is great >> yeah [snorts] and I mean um when you say transcribing by the way you mean like They're literally like doing this on the notepad. I mean like out of the >> Yeah. So like when we were for example in the control rooms at City of London police like we were quite surprised how like manual that is because like the people in the control room would be like listening and as they're listening they're like typing and talking which fine but like I think there's just a lot of lot of things that are going to get improved in that sense

as well which is great. >> Yeah. Yeah. No, I think that's um that's something that I uh I noticed when I uh I guess I've been working in just even I work in tech practically with what I do and even there's people who are in tech that are like not using AI transcribers and stuff which I find like fascinating like I find like fascinating that's not just the immediate thing but I I could definitely see the police being behind >> group of people way way more even. >> Yeah. Yeah. For sure. So, just kind of moving a little bit more into this as um you know, it's it's been how many months that you've been in uh development for this? >> How many months I've been building this? >> How many months have have we been working on this in general? >> I think it's

been now three years almost. >> Oh, three years. Okay. So, about 3. Okay. 37 months. Um >> three years and I would say like two and a half full-time. >> Okay. Got it. What has the experience kind of been from the standpoint of as AI has evolved? Because you just Huh? >> Yes, sir. Sorry. >> As AI No, you're good. As AI has evolved, you just mentioned how, you know, like the AI was the part that maybe you hadn't considered the most. As AI has kind of improved, how has that impacted the product and made you adjust your way of thinking too? Because I'm guessing, you know, reasoning battles kind of came out this year. Um, if you're saying you've been doing it for >> the last 3ish years, um, I'm guessing at the beginning AI decently rudimentary in comparison to now from a model

standpoint. So, I'm kind of curious how that kind of impacted the product from a functionality standpoint. >> 100%. I think that's been a lot of impact. Like I feel like our entire like almost AI architecture has improved so so so much because we were just starting with like very simple things like for example like spotting um keywords like from our data set of like things that would um say what like that it could be potentially dangerous situations. Now the option for us to combine that multimodel situation that we have like a video and audio but not just that but we have that AI agent reasoning behind it like that has just improved entirely the scope of everything that AI would be responsible in the product um and that it just adds like it's it adds way we're ready for more complex situations because if you

only had let's say audio and you were only purely looking at that there would be a lot of context that you would be missing for that dangerous situation where you have that agent that sees the audio and the video part and can draw the conclusions. Like I feel like that has been the main like main uh evolution within the AI for for >> Yeah. No, I think that's that's interesting to me because it's kind of you have to probably draw an interesting this is a followup question on the tech too. Just kind of curious. So, >> it's got to be hard though in the context of what's going on >> with reasoning models to analyze the situation fast or is it not or how how is that kind of impacted because obviously with reasoning models it it takes a longer period of time. So, I'm

kind of curious how you guys are kind of managing to do it in a timely manner. You know, >> I mean, we're doing we're working working a lot on the edge AI aspect as well. We don't have everything on edge right now, but there's I would say 20% right now that is on edge. So, that's something that we're tapping to uh way more than before. Um, but that obviously that kind of goes hand inhand with how the hardware develops and we're quite reliant on the hardware being really really good to handle um Edi. But in in terms of like for example the audio and video like so far that has not been an issue or like as in obviously we're at the point that we are going to be doing a lot of testing in the upcoming months. So I feel like that's going to

really give us the feedback loop that we're looking for but ultimately like timing or the reasoning hasn't been something that we've been struggling with. It's more like us optimizing how much we can move as well on edge because that's way way way faster. So for example, we have uh safety words that you can use right now that would be automatically identified um with our edge AI. So that's for example I I don't know by how much but way faster and in those moments when it really matters that's what we need to be super super quick. >> Yeah. So edge AI is not a term I had really heard until you just said it but is it like a local AI type of processing thing or >> so that would be having the AI ride on the device. So right now we have uh EIM on

the device. So we're communicating with like obviously our with the cloud and that's where the AI sits >> but we have small amount that is on on the edge and making decisions way there way faster but because it's very heavy we're doing just a tiny percentage of that. Um so we have a person in our team that is very like experienced in terms of that partic particular like angle of edgei. Um, so we're constantly like optimizing and looking how what more we can do there um to make it even faster. >> Wow. Yeah. No, that's um that's incredible. I didn't know even know that we were starting to to get there. That was not it's not necessarily in my um forte of understanding. So that's that's really cool. Um is there anything else like that tech-wise that you think would be kind of a unique

thing that you're trying to do to um I guess uh stand out? I don't want to say versus the competition because I'm not quite sure if there really is much at this stage. Um, but just like other cool stuff like that from a tech side because I thought that was really cool. >> I mean from the tech side what I would say what maybe let's say personally I find the coolest about um the specifically our AI is that uh and this is a bit more from the I would say commercial like user perspective is that in our case the more women wear the badge it it's helping us constantly improve the model. So the way we're like talking to our users about it is like whenever you wear the badge, even if something doesn't happen, but just you having and collecting the data is helping

improve the model and essentially helping maybe protect another woman. So making the user feel like they're part of creating something that can be of help to someone else at a different point. So it helps also with the adoption, right? Because then you're thinking like even if something minor happens or if something happens I would be able to then then you know with this having had the badge having had it on improve the model I'll have help the NF model be stronger and better and that it feels like a collective sort of effort to make it even more efficient. So I really like that angle that it's almost like a node of these like data coming together but it makes you feel like you're playing an active part. And what do you um so from a release standpoint on your website it's like it's like pre-order

but like how is the testing gone? Like how many units are you kind of working with at this point? >> So we are currently so we're going to be kicking off our proper like very closed uh beta testing in January. So we're receiving the prototypes now this month. We're going to do first internal testing and then we're going to do testing with a bunch of users and then based on the feedback we're going to just do the necessary improvements. make sure really that the badge is from start to the beginning perfect for the user that there's no any blocker is like from from the from the hardware perspective but also from everything going on in the back end and then we would be looking for to launch Q2 Q3 um in 2026. So that's sort of our plan that we're looking at right now. The

pre-orders are going to we're going to open the pre-orders as well a few months before before the launch. Um, but more details on that I think we're only going to reveal in a couple of months. >> That's okay. Yeah. Yeah, it's totally fine. Um, interesting though. I I I appreciate that context. I think um it's kind of cool to see how the whole thing's kind of laid out and you said you've been working on it for 3 years, right? Um, basically. So, how does it kind of feel that it's kind of finally coming to practical fruition? >> Honestly, at the same time, exciting and scary. just because I feel like it's like a lot of different like we're testing different things and we're like okay like the moment it's out there it's going to be whole new set of challenges and things to be conscious

of and worried about. Um but at the same time like I really want to see it and use and see how we can even improve it and using it. So, um, yeah, excited and, uh, yeah, more more more excited than scared, I would say, for sure. >> More excited than scared. Okay. Yeah, totally fair. It's, um, just really cool, um, to, you know, working on something for that long of a period of time. I'm sure like things can can feel like restless in some respects, >> especially with hardware. I feel like hardware is just [clears throat] always takes time. and they say the like obviously that everyone always says hardware is hard but it's also hard because it's like it takes time before you can see the the pieces go into fruition and that's a frustrating part that you've been working on so long and

you're just like I want to see it out there I want to see it out there but you cannot step skip all these steps that have to happen before from the testing to to just like for example the amount of time that we spend on getting the designs right because yeah one thing is the the electronics and everything but like you have to make sure the designs are very user happy, user approved, but also they work with our attack that we want to have inside that they're also secure enough for the criminals, for the perpetrator side. So, there's been a lot of different like steps that needed to happen that were so important before we can even get to the the point of launch. So, um yeah, it's just lengthy hardware journey, but um still still very exciting. >> That's very exciting. What do you

think is the um been like the I know obviously long-term vision could be kind of a hard thing to get the answer on even before you start, but what is the the long-term goal? Like what would you imagine uh in a perfect world kind of the uh uh company looking like in five years? Yeah. I think for us it's always been like we want to see the ENO badge. So, our wearable just being a no-brainer for everyday people's life. Like, it's almost like we would always joke with the co-founders like one day when you're on the tube and the girl gets off the tube and clips the badge and it's just like such an automatic thing to do. I think that for us would be success. It's like almost it becomes this unseparable part of female's purse. Like, you have the keys, you have the

phone, you have the AirPods, and you have the email badge. I think that's ultimately like that sort of integration as a no-brainer um safety companion. um that's something that we would want long term. Same as like Whoop now and Aura Ring have become like a no-brainers when it comes to like everyone should be tracking their health and they kind of changed the whole way we look at as a health as not nice to have but must have that you like know if everything is fine and that you are optimizing for for your health with the data that you're getting. So I want to see the same for the personal safety. It's like thing that people like should always think of and should always also like focus on their personal safety and how can they optimize that. >> Yeah. No, totally fair. I think that's um

that's a really good uh uh thing to to aspire to. Um it would be I think obviously it'd be a big improvement on what we currently got. um just like a like a a [snorts] momentary uh overt aggressiveness or um maybe a momentary you know like freak out and you know it could go it could go poorly um I think without that. So it's it's it's really admirable. I know we've talked a lot about hardware. Um this is a product that our company and >> a show that talks a little bit more about software. So on a personal level, obviously you've interacted with your AI in that physical sense, but I always like to ask this question of founders as I close out the show. What is your personal favorite um AI app or tool right now that you're using that's not your own one?

>> My personal favorite AI tool that I'm using at the moment. Yeah. Mhm. >> There's there's this one recently that I started using, I think maybe last week, but I've really liked it because it just like I feel like for my needs, it's it's very it's very like the painoint that I have is super well answered by this tool. And if I'm saying it correctly, it's called Yapper AI if I will have to double check. But essentially what it does is like I'm much better with my words than written form. So when I write emails like I don't put too much effort into my sentences being like super well structured. So like I hear it first and like I'm write it as I would speak it. Whereas my other co-founder is he is text first and then spoken version. So his emails are super well

written and I'm just like I'm way more on the yapper side. So, this is essentially like you would just yap your thoughts to this app and then it would write a very nice sophisticated email um through it. And I think that's just um I mean I mean on personal level uh I'm a very much voice messages person but I know that a lot of people find it frustrating when they receive a twominut podcast, three minute podcast [snorts] uh from a voice message perspective. So I think this is this is something that it like kind of hit the spot for me. >> Very cool. Well, um to close things out, tell everyone uh where you can where they can go to check out what you guys are doing and um be involved in uh enough. >> Yeah. So, by the time this podcast gets released, we're

going to have a brand new website, our landing page, which is going to be called eno badge. Eno uh badge.com. And that's where you can read more about the product. you can see um all the details as well as you could um sign up for the weight list so you can get notified the moment uh the moment we launch and the moment we open the pre-orders and at the same time to follow the journey and stay close with us you can you can follow the sort of behind the scenes of building building an F and the ENO badge through my through my um founder founder Instagram which is under ena the lower dash founder and that's more for the people that like to see the you know the side things not just the product but everything going on behind um and the work that is

being put. >> Absolutely. Well, with that being said, thank you so much for listening watching this episode everyone who was able to get a chance to follow through to the end of the episode. Please make sure to leave a like, comment, and also leave some reviews on Apple Podcast and Spotify. That helps us reach more people like you who want to hear about cool stories like this one. Thanks for watching. See you in the next one. >> Thank you for having me, Cho.