352 - Are you ready to see the next revolution in packaging testing?!? Ankit Dhawan from Blue-Pill.ai

Speaker A: Hey, uh, good morning or afternoon or evening, uh, whenever you are listening and wherever you are, welcome to another wonderful episode of the People of Packaging podcast, recorded right here in the Label King Studios in Salt Lake City. I am your host, Adam Peek, and today we are going to be joined by Ankit Davan. Uh, Ankit has an incredible new software technology that is going to deliver objective, true data about packaging design, amongst other things. We're going to do a live demo of what his software does and how quickly, uh, your brand can get results. His company is called bluepill AI. And I am incredibly excited for this episode because this is solving a, uh, pretty big problem in our industry. So without further ado, let's meet Ankit.

Speaker B: What we're doing with Blue Pill is essentially just combining my frustrations on user testing and insights tooling processes world with my love for AI and psychology. Essentially, what we do is we build a digital twin of a brand's target audience so that they can run any kind of package testing, concept testing or even behavioral studies. We just want to help brands understand the consumers deeply, so that have to go on cutting stick. Hey, Adam. Um, I'm good.

Speaker A: How are you, sir?

Speaker B: I'm great. Thank you so much for having me on this.

Speaker A: Yeah, this is, this is exciting stuff. I mean, I remember the first time we met and you said, hey, this is what I'm doing. I was like, that is a huge problem in our industry. Are you sure you're going to do it? Because I don't, I don't want to start bringing you on here and then have people be like, oh, great, yes, do this, do this, do this. Uh, because I think that's probably what's going to happen. So before we get into what you're doing and the problems you're solving, tell me a little bit about yourself. Where do you live? How did you get to the place where you're developing software for the packaging industry?

Speaker B: Yeah, absolutely. So I'm originally from India, but moved here to the US about 12 years ago. So came here to do my master's, went to Cornell. And then how I got into this was I used to lead product for the global marketing team at Amazon. So through that we used to launch new countries and without ever stepping foot into them. So did a bit of consumer insights work over there. Absolutely love the psychology of it, of user testing, but absolutely hated the processes and tools that were available. This is like about eight years ago now. And then I pivoted into AI pretty, uh, early. These are pre chat sheet days, so not as cool as how it is today, but I let AI for AWS essentially Amazon build some of the first artificial voices we built. And what we're doing with bluepill is essentially just combining my frustrations on the user testing and insights tooling processes world with my love for AI and psychology. Essentially what we do is we build a digital twin of a brand's target audience so that they can run any kind of package testing, concept testing or even behavioral studies. We just want to help brands understand their consumers deeply so that have to go on gutting stick. If you think about this industry of uh, insights and market research, $140 billion are spent on this every single year. And I'm sure a good chunk of it on package testing. But it's only reserved for the biggest brands, right? Each packaging study is about 15k if you do it properly. And packaging and concepts innovation is such an iterative process where you're not running these iteratively. So with bluepill, we are enabling any brand to literally upload their packaging or concept or behavioral studies and get insights from their digital twins of their audience.

Speaker A: That's amazing. And uh, it's funny because I've done a lot of uh, speaking uh, around the, around the world about not uh, just packaging, but also different sales processes and marketing and sustainability. And every time I come back to this point, which is get to objective data, something that is objectively true, um, as fast as you can, and then build from objective data. And so you talk about the advancements of AI and we're seeing more and more AI used in packaging, um, design and branding. And maybe not the final creation, but a lot of iterating. But it doesn't really help if you iterate 15 different versions of your packaging, but you have to go spend $15,000 on each testing. Uh, it makes a ton of sense to say, hey, we have 15 different versions. Let's run 15 different tests 15 different ways and let's do this in what, 15 minutes or less. I don't know how long it takes but um, you know, potentially pretty quickly, right?

Speaker B: Yeah, it should take under 10 minutes.

Speaker A: Amazing. Um, I need to, I did go to an event last year that I don't remember if I mentioned to you or not, uh, but I'll give a, ah, shout out to the, uh, it's the Flexo Experience center in Atlanta, uh, Georgia area I think. Was it in Atlanta? Pretty sure it was in Atlanta. Anyway, um, we did this, we did this like crazy brand design thing using AI, using expanded gamut plate Making where groups of people could create a brand, tell the story, have AI create artwork. But then, man, if you added this on the back end of that, and then AI came back and said, um, and they had a little bit of it with like, some. Some heat mapping, but it was not nearly as robust as what you deliver, then they could take all that data and say, okay, this is the one that's going to land. Um, um, so I just. I think it's really cool. Um, and where. Where are you located? You said you were in Cornell. Are you. But you were there. And then obviously I moved to the

Speaker B: other side of the country. I'm based in Seattle. Our whole team is based here. Uh, Sunny Seattle, as they said.

Speaker A: I don't think they say that. I don't think they say sunny Seattle. Hey, but wait, I want to talk about the Sustainable Packaging Podcast with my good friend Corey Connors. If you're watching this, just look at that. Look at this wonderful logo, this wonderful face. Sustainable Packaging Podcast is brought to you by Atlantic Packaging. And listen, if you care about the future of packaging, and I do, you do too. You're listening to a packaging podcast, then you need more. You want to check out the Sustainable Packaging Podcast with Corey Connors, presented by Atlantic Packaging. Corey talks with industry leaders about what's actually working in sustainable packaging, from better materials to smarter design. And he gives actionable insights you can actually use. It's informative, practical, and honestly, it's pretty fun. Who thought packaging could be fun? I know I do listen and subscribe to Sustainable Packaging Podcast wherever you. Whenever you listen to podcasts. He's got episodes that are releasing weekly. Check them out. That's fascinating. What? I think we talked. We talked about this earlier. What part of India did you grow up in?

Speaker B: I grew up in Delhi, the capital.

Speaker A: Okay, got it.

Speaker B: I don't know if you've ever been, but should be on everybody's bucket list to travel to India once in their lifetime.

Speaker A: I have been to India five times and had, um, the unfortunate experience of Canadian connecting through the Delhi airport. Um, and that was an experience that I will save for another podcast. But it did involve me, uh, essentially getting into a shouting match with an individual there who wouldn't let me through because, quote on goal, Andhra Pradesh, which is where I was going, is not a real city. And I was like, I have been there, sir. I. I pulled up my phone, I was like, do you want me to Google it? And he was like, it's not a real city. You can't go through it. I'm like it, there's 500, 000 people that live in this city. It was crazy. Anyway, speaking of the other 5 million

Speaker B: people live in that city.

Speaker A: Well, in Delhi, I'm talking about an ongoal, right? There's, there's, there's a half million people in on goal, Andhra Pradesh. Anyway, I was, I guess the story did make it on the podcast. Uh, uh, and, uh, anyway, we could talk about India and um, between northern and south Indian food and cuisine and culture, but we won't get into that. Maybe some other time. Um, so speaking of food and, and some really cool culture, what we're gonna do is. So I have, uh, one of, one of my good friends. Oh, oh, look at that. I can just pull it up right there. Um, is this company. If you're watching this, you can see it up here on the screen. If you're listening, it's called miss Essie's Barbecue M M I S S E S S I E S Barbecue. They make some of the best barbecue sauce I've ever tasted in my life. Um, and so what I have asked Ankit to do is say, hey, could we run a live demo of something that would happen because Ms. M essie's is a small brand. They're in some retail locations around Utah and surrounding states. It's not this massive, you know, brand with a huge budget. Uh, so this is kind of a cool little test case here. So, um, we've got their original pit, original, um, barbecue sauce that we are going to run through because I think we could talk about it, but it's so much better to show from start to finish. What is it going to look like? Um, and I know that you have your screen. We've lost your screen sharing. If you want to pull it back up.

Speaker B: Pulling it back up.

Speaker A: Um, you said this is going to be. Here, let me, uh, put this up here on the screen. Um, we are seeing now. This is blue pill AI. Is that correct?

Speaker B: That is correct.

Speaker A: Is this because of the Matrix?

Speaker B: That is because of the Matrix. If you take the blue pill, you stay in the simulation and we simulate consumer behavior. We want everybody to take the blue pill. Got it?

Speaker A: Okay. One of the greatest movies ever made, especially for its time. It was unbelievable. Um, are you able to zoom in even at all on your screen? Perfect. Walk us through what a user would do here. We're in the simulation. We've taken the blue. We're staying in the simulation. Um, let's talk through what a user would do here.

Speaker B: Yeah. So before we even begin this step assumes we have actually already built a specific audience for Mrs. In this case, we are just going to use a generic modern consumer who buys natural organic to see how that specific segment is going to react to the packaging and how they compare to their competitors. So at this step, we already built the audience and the idea is it's very simple. Like a brand would just come onto this page. Let's say we're going to do this. Ms. SC's brand name, Ms. SE's packaging review brand is Ms. Essays goal is to, let's say, understand what drives purchase intent in a package. Right. And then it's on the.

Speaker A: On the goal. Is that sort of, um, like a conversational. Like, could you, could you type, you know, sort of like everything that you want to see, or is it better to keep it pretty narrow, focused?

Speaker B: Uh, it's better to keep it narrow focus and high level. So let's say if the goal of the packaging reset is a redesign or launching a new product, every packaging has slightly different goals. So you can just put that in over here.

Speaker A: Okay, got it.

Speaker B: Yeah. And then we just upload. So the image which you put up. I'm just gonna, uh, put that up on the screen. Oh, this is not the one. Target front. So this is the front. We'll see all of these later.

Speaker A: And then where did you get these images, by the way? Because I obviously have the. I had the artwork, but yes, I just googled it. Amazing.

Speaker B: Ah. Uh, and then this side of the. So we have all of these images all set over here. And then I'm also going to upload the packaging of their competitors because they're going to get, like a rich understanding of, uh, who the competitor and how they're doing against the competitor as well. So I took the liberty of assuming some of their competitors.

Speaker A: Sure. Uh, we got it. Looks like we've got, uh, Kinders, Sweet Baby Rays. Uh, yeah, uh, a bunch of. So these are. These are some larger. That they would have to compete with on. On the shelf.

Speaker B: Yeah, exactly. And then we just continue. We are selecting an audience. So like I said, we're going to select the natural organic, modern shopper for this specific test. If we were actually working.

Speaker A: You want to select the Seattle Seahawks, uh, season.

Speaker B: Uh, we did have a conversation with them. So that's why, uh, that's.

Speaker A: That's awesome. I love it. I love it.

Speaker B: And then again, the whole idea is if we had actually worked with them, they would only see all of their segments. They would be targeting different segments in this case I'm just going to select all and then go to review. That's pretty much it. We have Ms. Essays understand what drives purchase intent. We have three sides of their packaging, front, side and back, and then competitor images. And that's it. We just uh, started the simulation

Speaker A: while this is running is less than two minutes. Wow. Um, so how would would the time be, uh, longer on like setting up the buyer Personas? I mean obviously you have things that are maybe in there that are generic like you showed, but somebody had already paid for some buyer Personas or um, how does that process work to set that up? That seems like that would be a bit longer than hey, here's Google Image, you know, Google search images and put it in there.

Speaker B: So the process is, let's say if we started working with Ms. Essays, we would need two weeks to build their audience. And in that two weeks we are actually going and interviewing the real consumers. We run deep qualitative 60 minute IDIs, which is like uh, qualitative studies. On top of that, we have ran like hundreds of packaging tests across different categories to understand what are the different elements which drive purchase intent. On top of that, we also run this, uh, for a few, in this specific, let's say source category. And we are fine tuning that specific model for this category. Everything which we're doing, the underlying data is real human behavior in this specific category, which is coming from the qualitative interviews from public social signals, from purchase data, and then our data set of running so many different packaging tests. So we take all of that, we fuse this into these digital twins. So if you saw on the last screen we had about 125, uh, natural, organic, uh, Personas. So all of these are micro Personas. They're representing a micro population, like micro segment in the population. So all of these people make similar decisions. Because underlying what we're doing, Adam, is we're trying to model the decision making. We're not trying to replicate what people are going to say because then if you show it anything which uh, that data has not seen, this will fail. So the idea is we want to model how a consumer makes a decision in front of a shelf. Where do the eyes go if they pick it up, what drives purchase intent, which claims, which marks, ah, when they flip the product, what ingredients help them make that purchase decision? So all of that is in our underlying data, which takes about two weeks of our time to collect and build.

Speaker A: Interesting. Yeah. And to your point, this is a very analog process currently. Right. So maybe when you were doing this at Amazon, how long would the test that we're doing right now. So let's assume that we've done, you know, you've got the two weeks. So we're at two weeks and then we get to this point and that's going to be, you know, whatever eight minutes. How long process have taken in previously?

Speaker B: Oh, at a minimum four to eight weeks. So once you, let's say have your package packaging ready or even parallelize it, you're first searching for somebody who can help feel this. You help create like a survey. Even if you like, even if you like, uh, you know, outsource it, you're looking for an a packaging agency to do this. So the cost would be easily like for the level of detail, which we have be easily like $25,000 and would take like six to eight weeks at a minimum. And once results come, should be another two, three minutes. You would see like the level of detail which we go into that level of detail. I think currently like no existing packaging test goes into this level of detail. But the biggest thing is now this brand can take these insights iterate and then run it again. They can do this hundreds of times because this is A.I. uh. So that is like this cost. There is obviously time that you'll get this in under like whatever, eight minutes. And then the ability to iterate is what resonates a lot with most product innovation teams.

Speaker A: Yeah, I mean that's kind of what I was thinking is once you've done, once you've kind of built out that foundation of the two weeks, you built up your buyer Persona. Now that's where the real magic is. Right. Because it kind of reminds me of printing. So in a flexo printing process, there's a lot of time in the setup, uh, so you could spend an hour and a half setting up press to run, um, like the Ms. Ses sauce labels.

Speaker B: Right.

Speaker A: But the difference between running 5,000 labels and 200,000 labels is, is maybe a difference of 45 minutes or an hour, whatever the number is. Right. It's like the, the scalability once you have the thing set up.

Speaker B: Yes.

Speaker A: It's the buyer Persona is now it's like, okay, what if we do this and what if we do that and what if we change and we put uh, uh, you know, organic on the front? Oh, what if we take off organic and we put no added sugar? Uh, you know, it's like all of these little things that you can just do and then run through. Um, to me is, is where a lot of the Magic is.

Speaker B: Yeah, yeah, absolutely. And what we are just running the packaging test here. Right. Like you can also run the claims test. What we do is run a max tiff on these digital twins. We have validated our maxdiff and everything what we're doing is validated against third party benchmarks. We're like 90% plus in terms of correlation to human preferences on a claims test. Also, uh, the last one we ran, the top claims are all the same, the bottom claims are all the same. So what a brand is getting on top of the packaging test? Let's say depending on what claims they have on there. All of that is also coming from real world data. So they can even pick which claims to put on or a conjoint analysis. What are the mix of claims, claims they should put to drive purchase intent.

Speaker A: Got it. Um, and I would imagine that as the, the speed of AI computing just continues to go up and up and I mean by the time that we, that this episode even comes out, there'll have been some change to AI where you're like, hey, it took eight minutes on the podcast and now it's six and a half minutes or whatever it is just because, yeah, it's such a rapidly evolving and changing, um, space and um, it's cool. You said you've been in it for how many years now?

Speaker B: Uh, easily seven plus years in AI. Yeah, it's crazy.

Speaker A: You're basically um, what the kids might say as an OG or an unknown.

Speaker B: I got into it way before it was uh, mainstream.

Speaker A: Yeah. And I'm noticing here too on the screen again, if you're uh, watching um, this, we've just been waiting for the processing to happen. But you do have a way to say, hey, I'm just going to type in my email address, I'm going to get notified once this thing is ready so that I can just go on with whatever my task was. And if you're like me, it's like, okay, task done, move on, I'm on to the next thing. I get an email. Oh yeah, my, my uh, simulation, uh, has run its course.

Speaker B: Yeah. Basically the reason of the time is all of us are relying on the big model providers. Right. They sometimes sequence stuff when the demand is high. So it's not like time is going down, time can go up. Also it's very interesting currently like how the model providers work. Uh, unless you're like one of the biggest companies who get priority are like startups like us will not be in their higher tier. So we can also get bumped in terms Of. Okay, we're gonna queue your workload right now. So that's why it's all estimated.

Speaker A: Yeah, but I mean, to your point, it's, it's gonna be, we're talking about like, you know, maybe instead of eight minutes, it's 15 minutes. Right? It's not instead of eight minutes, it's four weeks.

Speaker B: Oh, yeah, it's definitely gonna happen in the next five minutes. Yeah, for sure. So.

Speaker A: No, that's awesome. Um, how about this? I'm gonna, uh. So we're not watching the clock ticked out. Let me know. Let me know once it's, uh, once it's processed, because I want to get to the results here as well, because I'm, I'm excited to see and to, um, you know, to share with Ms. Essie's what, what some cool stuff is. So while that's happening, talk through the, the commercial model of this. Right. So I know we've mentioned 15 to $25,000 per test. What are you guys charging for these tests? Is it a SaaS? Monthly Subscription? How does that all work?

Speaker B: Yeah, so we, we charge a yearly subscription. Uh, it starts at $18,000, but the sweet spot most brands are paying us are $27,000, which includes your package testing, concept testing. And they can run 180 per year. So it comes out to be about $150 per run compared to like 15k per run. So essentially most brands either never do the package testing or if they do, they do it just once. Here we're enabling them to iteratively kind of do it at scale. So for even like smaller brands or startups like, this becomes a super valuable tool where they don't have to go on their gut instead, because all of the data which is coming is coming to real humans, and all of our models are built on, grounded in human data. They can comfortably believe what our output is and then go to the next step.

Speaker A: Awesome. Has the simulation finished?

Speaker B: No, it's still running. Should one of them. Yeah.

Speaker A: Oh, I thought I said I'm trying to like, follow on this little tiny, uh, screen. Um, and so. Okay, so, uh, you said 18 or $27,000. Roughly. 150. And, and I mean, you could do a test essentially every other day if you wanted to at, at 100, 180. That's, um, that's crazy. What would you say is the, um, the, the biggest, um, your, your biggest issue in kind of getting this to market and getting it to scale? Besides being a guest on the People of Packaging podcasts and me not scheduling things in a timely manner. Uh, but what would you say has been the hardest part? Like, what would you wish, what do you wish that people knew about your software?

Speaker B: I think dual things. A, I don't have any CPG background, so I don't come with any network in there. So just getting in and getting in front of customers is difficult. But beyond that, like, I think there's a lot of noise in this specific area. Like, people get really scared of AI. What. My only message would be that everything which we do is again, baked in your consumer interviews and data. Like, these are not random chat, GPT or some prompts. This is modeled specifically on your specific consumer so that you can go deep, ask any question anytime for a fraction of the cost, so that you can never ration research or you can just go to market with the voice of customer in every decision.

Speaker A: So would let me, let me kind of play not, uh, devil's advocate, but just, uh, a, uh, concerned AI person. Right. I mean, I'm in my mid-40s, so I'm like, I think AI is cool. Yeah, use it. But I'm also part of the generation that's just like, we don't need AI. Um, so, so if, if I'm a brand. Oh, it looks like it's done. So we'll get to the processing here in a second. Uh, so if I'm a brand and I've got a new product that I'm launching, how do I know that all these iterations are staying confined and aren't leaking out into these large language models so that somebody could. Ms. Essie's going to come out with the greatest thing ever and they don't want you to know it. What's the security look like there? Then we'll talk about miss Essie's and the findings.

Speaker B: Yeah, no, that's a great question, by the way. We are far more safer than running a packaging of concept test with real humans because that can get leaked here. Everything is guarded in our encrypted, proprietary kind of environment, everything the company shares or all of their data is their data. We never use it to improve our models or obviously any other one. So they are far more safer running concept or package testing with AI than they are with real humans. We've heard of stories where people ran a concept test with real human and got leaked, uh, because some competitor was obviously taking the concept test as part of the panel.

Speaker A: Yeah, no, I, that's, that's crazy. I, I one time, uh, I'm gonna pull this up and kind of wrap up the story. But, uh, I had a customer of mine and their most like, hated competitor. Uh, there was somebody who brought in like some samples to be like, look how great of a printer we are. And they brought in samples of products that hadn't been released yet. They were prototypes. They were like our prototyping and they were like, cool. Now we know the products that our competitor is going to launch in four months. And that guy shows me this and he said, I will never trust this company with our information. That's crazy. Um, anyway, all right, so, uh, the results are in me through what we are seeing here.

Speaker B: All right, so this is just a review of the front and the additional views overall. What you'll get up front is a summary of what works and where are the focus. Okay, so the first thing which was working well is that the plastic squeeze bottle is obviously like super convenient. So that's working in their flavor. The flavor clarity, meal guidance, nutritional visibility. All of these are good. Uh, it said the gray sketch portrait on the front panel is exceptionally pale and resembles a printing smudge. So we can see some. Oh, uh, oh, on the background. Yeah, so it's because it's a bit pale. So that's uh, showing up as there's like visual clutter, fine horizontal gray lines, uh, text orientations. I think. Oh, it's talking about the orientation which is like vertical. So that's going to be difficult to read. But honestly, the meat is in the very detailed analysis. So I'll walk you through like section by section. Overall, our tool essentially has been built on the best packaging out there, which are doing well. So overall, like, they need to do a packaging needs to do eight jobs in order to drive purchase intent. So they're doing a pretty decent job on comprehension, value, justification, user certainty. But there are three things where they really need to kind of, you know, double down on. And all of this, you can actually open here and see what this means. So packaging relies on heritage storylining and process descriptors rather than quantified claims and stuff. So maybe like, because this is also. You have to contextualize that this is a natural, organic, modern shopper. So if they're looking for organic logo, you know, stamp and stuff, so that could be there, uh, differentiation and positioning. So all of this, again, we don't have to go through all of this. Otherwise we'll be here for another half an hour.

Speaker A: That's true.

Speaker B: Uh, and then we also give like a quick checklist, you know, what's proof, clusters, ingredient transparency. So where they're doing well or not. So they can just kind of go double down and then uh, I just will have to go. So this is like a shelf test. Okay. So I think it's not doing that, but it's doing pretty well if you look at this shelf. I think you can also like, you know, judge where the eyes are going mostly. So sweet Baby Raise is winning the shelf test, but Ms. Essays is not doing badly at all in the shelf test. And overall you can also double down on why our tool will tell them why they're winning or not winning. So overall, uh, their strengths are the high contrast model, but they have low brand recognition obviously compared to others. Uh, and then they can also look at them versus competition. So uh, research shows in order for a packaging to do well, there's these four different pillars which they need to do well on and then they can see how they are doing against their competition and then again iterate and uh, you know, double down on that.

Speaker A: So and the crazy thing about this is, so they can get this information on their current packaging and then maybe go work with their creative team and instead of saying, hey, can you print off, uh, you know, 100 of this one and 100 of that one, we're going to put it on our bottles. We're going to do all these live testing. It's like now they can digitally create these renderings, essentially put those in and say, okay, well what if we do this? And it's like, okay, this will be better but uh, your brand recognition is going to go down or this is going to happen, or the amount of saved money is big just in terms of prototyping. And then also the amount of potential earned, you know, earnings is high. Um, because you can now make data driven decisions on what is historically a very subjective. I feel like this is going to be really good.

Speaker B: Well, exactly.

Speaker A: Yeah. That's cool.

Speaker B: And again, like the data is coming from hundreds of real interviews, thousands of packed models. It's like, you know, it is the wisdom of the crowds here. It's not like just cutting stuff things. And then they can go very specific individual level. So what do you like about this packaging? This is all uh, you know, for, specifically for Ms. Essays. What do you dislike about this packaging? What's the emotional response when you look at this? Uh, what promise do you think this is making? So obviously this is what they wanted to do. So they're doing really well on the classic southern flavor. And uh, what are the key purchase drivers or barriers? So you can like really go, you know, really, really deep. Uh, and then side of the pack analysis and back of the pack analysis. So again, it's a super long, detailed report, but very, very specific. They can just read through only the recommendations if they want to go deeper. They can also chat with uh, the twins, uh, anytime they want outside of this packaging to understand, uh, what claims should we add or what else can we change on this packaging just outside of this report as well.

Speaker A: Oh, interesting. So they, so in this case, Ms. Essie's would be able to have a conversation with the digital twin of this natural organic buyer, um, and, and ask questions and then get feedback live, which is crazy. It's crazy. Um, wow. I, I think this is a, um, it's, it's a really remarkable tool. Honestly on Keith, I mean what, what you and your team have built there, um, is that the fact that we could do that in a 30 minute time period, obviously the two weeks had already been happening. But does, does it actually have these sort of standardized people groups? Like if, if somebody said I don't need, I don't need the whole two weeks, we have a pretty ubiquitous product. Um, I just want to use this, you know, this natural grocer thing and then I'm just going to start peppering it that those exist in the system.

Speaker B: Yeah, they can come in and just work off our existing audiences. But being honest, like the maximum value they're going to get if we are going to build it for their specific target audience. Because if I had to guess, natural organic shoppers might be a percentage of Ms. Se's target, uh, audience, not the whole, like the whole piece of the audience. Right. But it'll still be like 60, 70% overlap of the insights.

Speaker A: Got it.

Speaker B: Yeah.

Speaker A: Well, um, that's, that's, it's, it's incredible. Uh, everybody go check out blue dash pill AI. Um, you can also find, uh, ankit on uh, LinkedIn, I believe. Uh, you're on, you're on that great website that every other social media platform seems to hate, but it's the only one that's like actually productive and not rage bait content. So um, I, I love this, I love what you're doing. Um, I think, I think, ah, there's going to be a time in my life where I'm like that guy was on my podcast once. I think it's very cool. Uh, do me a favor, don't uh, don't exit the studio if we forgot to tell you that before the call, um, when we wrap up. But once again, everyone go check out uh, Bluepill AI. I'm sure there's ways to contact them. Sign up and get in touch with, uh, what it is that you're doing. Thanks, Ankit.

Speaker B: Thanks, Adam. Cheers.

Speaker A: Yep, cheers.