AI Product Creators

Dhaval Bhatt
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Welcome to the AI Product Creators podcast, where I interview AI product creators and innovators to learn their best practices and tips for creating successful AI products. In each episode, I'll speak with a different AI product creator to get their unique perspective on creating and launching AI products, from ideation to development to marketing and beyond. Our guests will be sharing their insights on the latest AI trends, key considerations and strategies for success, and real-world stories from their own AI product journeys. Whether you're an AI novice or a seasoned creator, this podcast will help you gain the knowledge and confidence you need to create successful AI products.

  1. 05/19/2023

    Transforming Creativity and Content Creation with Zach Hanson - A Deep Dive into the Future of AI Product Management

    Zach Hanson is an expert in artificial intelligence and machine learning product management, with experience developing AI solutions for Fortune 500 companies including IBM, Brightcove, Capital One, and Wells Fargo. He holds degrees from the College of Charleston and Johns Hopkins University. In today's episode, We discussed power of AI, Zach discusses how it aids in tasks like content parsing, summarizing, and producing video trailers. He also explores the interconnection of different AI models, and the rise of content generation through freeform speech. We discusses how AI technologies, like ChatGPT and GitHub co-pilot, can streamline creative content creation and refine stories or code. Finally, for those looking to enter the AI product management or creation space, Zach advises building something to understand core product fundamentals and getting comfortable with data. Tune in to hear Zach Hanson's insights and experiences in building Inworld AI and how you can apply these lessons to your own product. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Zach Hanson: • LinkedIn: https://www.linkedin.com/in/zachary-hanson-a1a761a3/  Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt  Transcript:- Dhaval: Hey, Zach, tell us what you've been up to. Zach: So everybody, I'm Zach Hanson. Dhaval it's great to see you again. You and I used to work together in a, past life in the AI field but What I've been up to lately is pushing for AI innovation within video, specifically at Brightcove, which is a great company, and working around how we actually build out and better experiences for our customers in the video space. Dhaval: Oh, wow. That's like the most cutting edge space in AI right now. The video and AI generated videos and all of that as we speak in April of 2023. What specifically is Brightcove's mission? And, yeah, if you could share a little bit about the, what is the product, what pain is it solving, and what are your customers, who are your customers? And then a little bit about where you are in the product space, like in the journey. Like, are you a startup? Are you, have you already found the product market fit? Are you enterprise? So yeah, any of that? So, a lot of questions, but just trying to understand the product story. Zach: So it is interesting, right? So we're a very unique company. So Brightcove's goal, to answer your first question, has become the most trusted company in streaming. So that's our goal, right? That's the big headline. That's what we're pushing and we kind of run the line of being a media company and an enterprise company. So from a competitive landscape perspective just to frame where we're at, would be the Vimeos of the world, Kaltura and some other companies that are providing OTT services to brands around the world. And Brightcove has actually been in the game for over 15 years. So to answer another piece of your question is we're a publicly traded company. We've been around for over 15 years and we've been providing these services for live streaming, for video on demand OTT for years, and it's pretty amazing. So we're one of the biggest little companies you might not have heard of. But with that comes a lot of responsibility around data because we actually ingest around one to two petabytes of video data every month. So we have an absolutely enormous catalog and data warehouse of videos, audio, all sorts of content that is being leveraged by our customers. Now, to answer one of the other questions rolled in there about our customers and some of the ones we can talk about, we help deliver video, great quality video with our encoders for the Olympics the year ago. Wow. Yeah, we've done that. We work with south by Southwest. So folks, if you've watched conference video from there, that's Brightcove under the hood masterclass for instance. So we have a huge list of really amazing clients who are doing All sorts of different things with video. And that's what we're trying to enable. Now, the other piece of your question, where are we at in the product journey? Are we a startup? Are we a mature company? And I would say from the delivery of video, we're very much a mature company. But when we start to think about machine learning and leveraging Models that are out there building our own models, we're really much more in that startup phase where we're trying to find the appropriate product market fit for the different types of models we might build or leverage the really immense amount of data we have to train models and do some really cool things. Dhaval:  Wow. Thank you. Thank you so much for answering all those questions. I threw a barrage of questions at you. Wanna dive in a little bit on your last answer here on the topic of being new to AI ml. And what I wanna understand, Zach, is what is the customer trying to do when they want to use the AI ML capabilities for Brightcove? What is the thing that's going on in their head when they are like trying to use your specifically AI ML features? Zach: So this is where it's also like an interesting story because there's a lot of stuff that we're focused on from a machine learning perspective, kind of under the hood, things that our customers might not know is being powered by machine learning. So some of that has to do with encoding and how we get the video to the actual end user in a very efficient manner or in an efficient manner. As far as doing CDN optimization and making sure that the ultimate end user, which is our customer's customer, whoever's watching video. Has a great experience and that's where the bulk of our effort's been. But when you think about pain points, as we think about becoming more of a media company, when we think about enabling producers of content to be able to do some really cool things there's really this kind of crawl, walk, run approach. One is when somebody uploads content to our catalog or their catalog through Brightcove, there are sorts of metadata that should be tagged in those videos. Oftentimes people are having to do that manually time stamping stuff or putting this as a certain piece of a sub catalog within their overall experience. So we're trying to do some automation through their of automating tag management to suggest to our customers tags they might need in order to ease the burden of some of the metadata management, but then you go up the chain to content itself, the video, and we start to think about object recognition and video. We start to think about segmenting video. So you can easily cut and pull out specific elements of a video. For instance, if you were watching a soccer match or football match I grew up in the United States, so I'm a little bit more used to American football and I've become a bit more of a fan of the universal football in the years, in the past few years. But you might have an hour and a half long game and only have two goals or none. So the ability to be able to search through a video and find that really intense moment where somebody actually scores a goal, be able to rip that out really quickly and repurpose that content for marketing is very powerful. And there's a lot of startups actually playing in that space and then you have the bigger players like ourselves, Brightcove. Then you also trying to play around with segmentation of video. So it really runs the whole gamut where we've been focused mostly on backend support, leveraging ml. All the way to that kind of front facing customer content production type of use case. Dhaval:  Yeah, I, that's amazing. You have, I can think of so many use cases. I was at a photo shoot video shoot event this weekend where I was hosting it, and we have like terabytes of video content that we created and now I want to create recap videos for that event. And I can imagine being able to feed something like that to your platform. Is that, am I getting it right? Would that be a potential use case? Is that how you It is parts out valuable clips. Zach: Exactly. And that is part of a potential use case that we're exploring. But this is where it goes back to being in that kind of pseudo startup space. Like with all the data we have. With the great customers that we have, there's a lot of opportunity there and we're still in that feeling out phase of saying, what are those pain points to your other question. And like the use case you just gave, that might be something at the top of mind for a lot of our customers. And that's where we're just starting to put the feelers out and understand how we might be able to build some of these things out and make sure we have the right product market fit before rolling something out to our broader customer base. Dhaval: Yeah, that's very interesting. Just like thinking for like content creators like myself. That event is an example of a use case. This podcast is an example of a use case, parsing out insights from this video, insights, and then publishing them. And then for courses that I create on product management and artificial intelligence, it's the same thing. I can imagine being able to give you a whole course and create a trailer for that. So there are a million use cases that you can be going after. Are you thinking of like any big use cases right now that you are willing to share with us, that you may wanna pursue in near future? Zach: You know, none that I want to talk about specifically for Brightcove. But there are a lot of really interesting things in that segmentation space that just interest me and that might be wrapped into something we end up doing with Brightcove. It might not, but meta just came out with a paper on Segment. Anything. Have you heard of this model that they've built? Again, it's in that segmentation space on video or, things like that. But with all the other models, stable diffusion. People are starting to piecemeal these different models together to come u

    22 min

  2. 04/14/2023

    This founder built a platform to create lifelike characters for immersive experiences | EXCLUSIVE!

    Kylan Gibbs is the CPO and Co-Founder at Inworld AI. He is the former Product Manager at DeepMind, Consultant at Bain and also Co-founder at FlowX. In today's episode, Kylan Gibbs shares his experience working in AI startups and consulting, as well as his time at DeepMind working on conversational AI and generative models. He emphasizes the importance of iterative processes, adapting to market pressures and user feedback, and the need for creativity in defining good content in the AI space. He advises aspiring product creators to focus on building something that validates their value rather than teaching others before learning. Tune in to hear Kylan's insights and experiences in building Inworld AI and how you can apply these lessons to your own product. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Kylan Gibbs: • LinkedIn: https://www.linkedin.com/in/kylangibbs  Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt  Transcript:- Dhaval: This founder and his team built a platform to create life-like characters for immersive experiences like games or books. In this show, Kylan Gibbs shares his thoughts on generative AI and how to manage products where you create immersive experiences. He also discusses his career journey. Kylen is a former product manager at DeepMind. He was also a consultant at And a co-founder at Flow X. Now he's a C P O and co-founder at In World AI. Hey Kylan, welcome to the show. Tell us about where you are in your product journey, a little bit about your product as well. Kylan: Awesome. Yeah. Thank you so much for having me. Super excited to be here. So At InWorld, we're building a creative suite of tools that allow people to build these AI characters and then integrate them into immersive experiences,  games, entertainment, enterprise experiences as well. And we started just over a year and a half ago.  And basically where we're at now is we have this studio where people can come in and actually craft their characters. We got integrations with things like Unity, unreal, where they can bring them into games, as well as Node for actually bringing these into web experiences. We've also then got sort of this arcade and, basically, which is a way to actually share these characters directly to the web. And then a suite of experiences that we're gonna be releasing this year that are self-produced. So we've got, for example, in world origins to sort of show off  the power of the product. And this is all to sort of say that this is kind of crafting that end-to-end user journey of being able to build these characters and then bring them into worlds. And basically where this has all been going is kind of setting the foundations of actually being able to not just create the characters, but, build them into experiences and deploy them scalably. And I think,  compared to a lot of the things you're seeing in generative AI right now, like it really is production ready. And we've been focused on that.  And so we've already seen  a lot of developers starting to churn out games and experiences now that are integrating in world characters. So it's super exciting to kind of see that. So, of course still early on,  I'm excited to see where it goes, but already seeing hundreds and thousands of users using it,  plus,  actually seeing live experiences is pretty magical.  Dhaval: Wow.  There's a lot there. So let me just quickly ask you a follow-up question on how you support game creators. Is that right? Or do you support all kinds of creators, like writers,  novelists, or just focus on gaming experiences at this?   Kylan: We're supporting really any type of creator. So ultimately,  we have users who are, for example, very,  well-known science fiction authors who are using their characters to iterate on experiences and potentially write new books with,  we have people actually building like AAA games and these types of experiences where you're building multiple characters and integrating them into worlds. We have entertainment companies who,  you imagine integrating these into parks or like live experiences where you're actually interacting with. And then we also have enterprises who are using these for things like,  brand representation, corporate training. So really we're open to any types of creators. Of course, we're really focused on  narrative oriented, immersive experiences, and our product is best built for that. So, you know games, narrative, entertainment, you can think about  the adaptation of movies and IP to  these experiences. And that's really  what  we're targeting. But we're really open to any types of creators, and  we're finding new ones every day.  Dhaval: Yeah,  I picked up a keyword there. Narrative oriented, immersive experiences that could be representing multiple customer segments. What is your product journey like? How do you go about defining your product capabilities with such a broad range, range that you know you could be? Kylan: I think that, so abstracting out of our specific use case for a second, like I think when you're building a developer tool, you always have two customers. You have to think, keep in mind, one is your creators, your developers, and the other is your end users. And so for us, our creator journey is really people coming in. They have ideas either their building an existing experience or they're ideating on a new one and they're using the studio and our characters to basically iterate on that and then integrate it through things like Unity and Unreal to actually bring those to users. And so when we think about success in that, it's basically: is this person able to create the character that they love and like, and kinda ultimately  represents the vision that they have and fulfills the purpose? And then are they able to integrate that and deploy that  successfully. Then you have  the actual end users, which are the people actually interacting with the characters that the developers built.  And that is really  like, is the interaction enjoyable?  Is the person you know staying around to talk with this character? Are they finding out what they need to progress through this experience?  You can imagine throughout a game you have someone that's a guide or  shopkeeper and they have to fulfill a particular role. Are they doing that successfully? And so you really have to balance the two of those. I think that's true for most developer tools, but it's kind of unique here because,  ultimately  there's a key part here, which is like, it's the generated content. So  it's almost like  we are allowing developers to create characters that are fulfilling the wants that they have for the users in the end?  And so it's always a little bit of an art and a science.  Dhaval: Yeah, there is a lot of art there, especially when it comes. The narrative, the experience, right? The character creation and then narrative and the experience.  What are some of the ways you create these characters that actually immerse themselves and are conducive to the experience?  Is there a difference in product creation? How do you actually adapt to the narrative or the experience that the creator is trying to have? Kylan: Yeah,  I guess there's two points, which is like design time and run time here. So at design time,  you could take for example,  Arah LA a large language model and generate sort of responses that are aligned with a particular character. But getting them to do that reliably and stick to a story and actually fulfill goals and actions is very difficult. So we actually allow users to come in and they can specify, for example, scenes for their story, and then the characters will actually stick to basically,  the kind of motivations and goals that they have for that specific scene.  Then we allow them to specify,  what is this character supposed to accomplish in this? How are they supposed to speak?  What types of things in the world might they be reacting to?  And all of that sort of is going towards  controlling and biasing the character behavior in a specific moment within a specific story. So they're  fulfilling what they're supposed to. And that's all kind of  the design side. When you're actually interacting with the character. Then  we introduce, for example, emotions. So the characters actually react with emotionality. We have voices that also integrate that emotionality, so the characters can actually, you know, you could hear when you upset a character, for example, and you can react to that. We then control  gestures and animations. The characters can actually react to you,  or  you can ask them to perform a particular action and they can actually act on that. And so,  that ability to actually have, and we'll be releasing this soon, and we have a new system where you can actually, for example, give a character a goal  and a series of actions that they can use to pursue that goal. And they'll autonomously pursue the goal until they've accomplished it, which is like a pretty magical thing if you think about the ability to actually create this living thing that's kind of,  pursuing goals  and motivations. But of course all of that comes back to the ability to actually drive this story or narrative forward, whether it's something like Assassins Creed or Far Cry or one of these games. Or you can think about even in an enterprise experience where you're trying to usher a user forward through sort of a brand experience.  All of it is really  the key point is that the characters are filling a specific purpose in some broader experience.  Yeah. And so we've got a huge amount of controls  that are available to enable. Dhaval: That's very interesting. You mentioned that you are able to add emotionality to a character. Wow. That blows my mind. Is there anything more you can share about how you go about doing that?  I'm

    19 min

  3. 04/05/2023

    How this founder built a personal language model to mimic your behavior, knowledge, and style.

    Suman Kanuganti is the Co-Founder and CEO at Personal.ai. Previous to Personal.ai, Suman also founded Aira. Suman holds his BE degree in Engineering, MS in Robotics, MBA in Entrepreneurship, and ten patents in emerging technologies as well. Suman also founded Aira. Personal AI is a GPT implementation designed to mimic an individual's behavior and to speak like them. In today's episode, Suman shares his goal is to create AI systems that understand and replicate users' communication patterns and cognitive abilities. Suman emphasizes that their language model has time awareness, allowing it to adapt its knowledge base depending on the user's age or point in their timeline. Suman also shares his some learning lessons for AI product creators. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Suman Kanuganti: • LinkedIn: https://www.linkedin.com/in/kanugantisuman/  Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt  Transcript:- Dhaval: So today we have Suman Kanuganti on the show. Sunman is the co-founder of personal AI, a product that he will talk about and, we'll learn a lot more about where his learning lessons bear, what his learning lessons bear and all that other stuff. So yeah, let's get started. Suman, would you mind introducing yourself and tell us a little bit about your product, AI product? Suman Kanuganti: Sure Dhaval. Thanks for having me. I'm Suman Kanuganti. My background is in engineering. Over 10 years ago I started creating companies. This is my second company. Previously I built a company called Aira, A-I-R-A. My philosophy always has been how do use technology kind, solve, hard human problems. Aira was about using technology to fill the gap of missing visual information for people who are visually challenged, such as blind and low vision. And personal AI is about augmenting people's mind where memory, cognition, and our time is limited. And we would want to augment that using technology by creating a personal language model of every individual that essentially learns to behave and act and learn to be you. So that's a little bit of my background and who I am. Dhaval:  Wonderful. Thank you, Suman. Thank you for that introduction. Tell us a little bit about what personal AI's narrative is like. What is the, who is your target customer? What kind of a problem, specific problem you are solving with that product? And what is the overall narrative? How is it different from your competitors? Suman Kanuganti: Yeah, totally. As individual people, like on a day-to-day basis, we create and consume a lot of information. Like, you know, a lot of experiences have a lot of conversations. But obviously, 80% of that is lost or forgotten. Our goal with personal AI is to be able to create a model. That actually learns your knowledge, your style, and your voice, more or less like to be a digital version of you. Imagine being able to surface relevant pieces of your own knowledge, on demand whenever you need it. Or imagine, you having conversations in a, chat or text message. Are people talking to you? Where relevant pieces of information when we start facing as you speak. So our intention is to be able to augment, humans with an extension of their own mind. Because one cognition is limited and then two time is also limited. You mentioned about the target market. Our goal is to actually go after everyday consumers. Our intention is to have everybody have their own personal AI. That is trusted by them. The data belongs to them. The model gets trained over a period of time and innovate kind of grows alongside with you. Unlike, public or general models that exists such as open AI, such as, Google or Alexa, which is mostly like trained on public internet of data. Personal AI is a unique model that also uses similar architecture such as GPT, but actually trains on individual person's data. And it does so stylistically, relevantly authentically to replicate as you would. So we are trying to essentially like replicate, your thought process and your mind and give you an extension of your. Dhaval:  Wow. So it is adding the stylistic and tonality and the personal attributes to your, to the replica that you are building for someone which is not there in current GPT or any of those products, right? So there's a bias that. GPT has very confident answers, but doesn't necessarily align with your style or may not align with the way you communicate. And what you are saying is that not only does personal AI helps you do that, but also creates the model in the first place using your personal attributes , did I get that right Simon? Suman Kanuganti: Yeah, totally. So you will create what we refer to as a memory stack, which is essentially taking all your unstructured data that you ever have in your digital world. Let's say you're having conversations online, you are texting with people. You probably have written a bunch of different knowledge pieces out there. And then we create this memory stack, which is essentially like a digital representation of your memory vault. In other words, we basically break down this idea of structuring your data into these blocks that is associated with time. And imagine over a period of time, as you create an as you learn. Your AI technically would also be training alongside with you. So it's kind of how. Conceptually we have architecture system. Dhaval:  Got it. Now, there are, if you were to dive a little bit into your products architecture or the core engine, the AI, since the audience of this show are the people who aspire to either create an AI Product. Or wanna add AI to their existing product, for their knowledge. If you were to share a little bit without getting into the confidential details about what does a product stack, what does it take to build memory stack? What does it take to build a knowledge replica, a knowledge brain summons, human brain, and human personality into something that you were doing, like what do you call that thing? The entity that. Suman Kanuganti: Yeah, I'll try to provide answers and then I'll try to provide some contrast technologies that exist out there so that we can wrap our heads around. The first thing is at the core, we are essentially an AI first company. We built an algorithm called. Personal language model. So we call it personal language model. This is in contrast or like kind of opposite in concept to a large language model. If you think of a large language model such as GPT 3 or any other open Language models that exist out there close to around like one 70 billion parameters in our case. Our language models around one 40 million parameters. It revolves around at the core individual's data and not public's data. And you can keep on adding the data to your model so that way it gets more sophisticated in regards to the purposes of your mind. You can go abroad and you can also like, go deep into specific topics itself. So yeah, at the core we build this personal language model for every individual to essentially like mimic the behavior, knowledge, and style of an individual person. And the transformer that we have developed, we call it generative Grounded Transformer. And if you think about like GPT as a generative pre-train transformer, the subtle difference of our transformer is that it is grounded in the personal data of you. And whenever I refer to personal data, is nothing bad. The memory is tag that I was explaining earlier, So every AI response that your personal language model actually generates, it has an attribution, and the attribution is nothing but attribution Back to the data or what"s? Data elements of what memories in within your stack is responsible for creating a particular response? One of the challenges for large language models is that there is no attribution, primarily because it is driven by aggregation of the data. And there is quite extensive anonymization that is involved. So technically, you cannot create that attribution and it's extremely hard to create that attribution. And our goal is exactly the opposite. We would want to have. That attribution, we want to have the ownership and we want to create that value to every individual consumer by creating their own individual model, at the foundation, it's personal language model. Dhaval: So this generated you, grounded trained model that you are referring to is that, Adaptive with human changes, human behavior changes over the lifespan of the user. Suman Kanuganti: Exactly. The transformer also has a sense of time. For example, let's say, if you're talking about AI maybe three years ago, How you refer to your transformer, how you refer to your technology, maybe different from your latest and greatest creations or thought process around your ai. So it has a time to decay component. So when you are indeed chatting with your own AI, it normally anchors around the latest and the greatest thought process of. How you would respond. However, let's say if you indeed are contextually trying to fit something from the past that happened like 10 years ago Then, we are probably talking about the first autonomous car, right? And my experience with the first autonomous car, then it will. It'll go back in time and be able to fetch that response for you. So kind of like designed to work very similar or akin to how a human mind would function. You can think about potentially being able to drop your AI at a certain period in time. Given these are small models and given as the data is going in on a day-to-day basis, there is a new version of the model. We are technically able to time travel. Your model, like let's say 2, 2020, and then when you are having the conversation, it would like replicate the information density as if you were to be functioning at that time. If that makes sense. Dhaval:  That makes sense. Yeah. So with this language model the way you communicate is it's having a time sensit

    20 min

  4. 04/04/2023

    What happens when a PhD Professor in Analytics launches an AI Writing Product?

    Martin Pichlmair is the CEO of Write with LAIKA, Associate Professor at ITU Copenhagen and Co-founder of Broken Rules. He Holds an PhD degree (Department of Informatics) in Vienna University of Technology. In today's episode, Martin explains that LAIKA is designed to make AI-generated writing more accessible and user-friendly, with the AI and the user working in a tight interactive loop. Martin highlights that their product uses a "no-prompt" system, which means users don't need to be skilled in prompt engineering to get meaningful results from the AI. Instead, the software handles most of the prompt engineering behind the scenes, making it easier for users to interact with the AI. Tune in to hear Martin's insights and experiences in building LAIKA and how you can apply these lessons to your own product. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Martin Pichlmair: • LinkedIn: https://www.linkedin.com/in/martinpi/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt  Transcript:- Dhaval: welcome to the call, Martin. Thank you for joining. Tell us a little bit about your product. Martin Pichlmair: Okay, so I'm Martin. I'm the CEO of Write with LAIKA. And our product is a kind of creative writing tool that is using large language models, in our case, quite small, large language models to, support writers when they get stuck or when they need more text, that is influenced by their previous writing. Dhaval: Wow. Okay. So when the writers get stuck or when they. Interested in continuing with the style and the tone of their previous work. They can use your product including the contents, the storyline, or anything along those lines. Martin Pichlmair: Yes. How LAIKA works is that you you upload existing writing. You have when you get, for example, stuck in a murder mystery because you don't know who the murder is. Funnily, we had that case twice already with users. And then you upload what you have written before and our, system fine tunes a language model With your text and then you can prompt the model to continue writing in your voice, in your using your characters. You mentioned using scenes you have been writing about in the past and very much sounding like you. Now you can do that with your own text. Or with the text of famous writers, we have, for example Dostoevsky in there and Jane Austen in there. And a lot of, all of them, of course, dead and out of copyright writers that you can also collaborate with in a similar way by asking them how they would continue a sentence, for example. Dhaval: Wow. So it has memory and context as well as style and the personalization built into it. So is that. Large language model that's very different from Chat GPT 3, which would spit out very confident phrases very long phrases. But they're also having the same style. Is that, how is that different from the large language models? You said that you have used large language models or you have you built on top of them or like, help us a little bit on how have you built this. Martin Pichlmair: So we've built this on very small, large language models. They're still in the same architecture and come from the same family, but they're very small because that gives us the ability to fine tune them very quickly. It takes like five minutes. If you upload , a half done book, for example, takes five minutes and you get your own, we call them brains because that's a nice metaphor. Your own brain based on your writing to interact with. Now, of course it has an understanding of the context, but it's not always super, like it doesn't have an actual understanding. It can just play with probabilities of words, just like all of those language models do. Dhaval:  Wow. Very cool. Let's dive a little bit into your product journey, is this your first startup? Is this your first AI product? Tell us a little bit about your background, Martin. Martin Pichlmair: So I have a weird background. I did a PhD in computer science originally at the University of Vienna, at the tech university, and then worked in academia for a couple of years. I got a little bit, I don't know I wouldn't say bored, but I wanted to do something differently. So I started a video game company and then after a year started another video game company because the first one didn't work out. It didn't work out, but it also didn't not work out. It was fine. It was just not meant to be a longer existing thing. The second one actually is still around. It's called Broken Rules and makes awesome in the games. But I'm not involved anymore because I decided at some point to go back into academia. So that's where I spent the last seven years until last year where I just realized with my partner, That we have a huge connection between what I was doing in research, which was using generative AI to create systems for video games and her background, which is writing for video games. So we sat down and, uh, started workshops during the Covid Pandemic when everyone was sitting at home. We started online workshops where we introduced writers. To, the newest possibilities in language models, using very, very clunky tools at that point. And after three or so of those workshops, we realized the workshops are always poked out, but it's really hard to work with the tools that are there. So we decided we have to make our own tool, and that became a research project that was funded by the Danish state, uh, in the beginning. With the intention of turning it into a product. And since last November, we founded a company and turned it into an actual university spin off that is based on yeah, research that is now working on a product that we will commercialize within the next month Dhaval:  Very interesting background. You do have like a very traditional computer science background, making you very competent in this area. Right. So, quick question. You mentioned that you launched this in November, but you haven't commercialized. It doesn't mean that the product has not launched yet. Martin Pichlmair: Yeah. We have a wait list and we have, a data with, nearly 2000 users. So there are a lot of people using it every day, but, it's not a commercially launched yet. We're still only free for select users. Dhaval: Very cool. Is this. Is this a self-funded or have you bootstrapped this whole thing? Are you intending to, or have you raised capital? And are you intending to raise capital as you move forward? Martin Pichlmair: Well, we got some funding from Danish State again. The program that we're in that funded turning research into a product last year that was still in the context of my university has a follow up program that funds your salary basically. So we are kind of weirdly half bootstrapped. We have no investor, but we have, our salaries covered by the Danish State but a very low salary. But still, it's good enough to, know that we'll be, we'll be around for another year at least while this funding runs. we're looking for investment in the moment. It's, we are talking to a lot of VCs. It is. It just takes a while it seems. Dhaval: Yeah. how is that playing out in this current market? Like how is that, can you give us have you done this before? And if you have, like how is it compared to the current market, if you can speak to that. Martin Pichlmair: So I haven't done it before, but a good friend of mine has a very similar company, actually a very different company, but also an AI company also in Denmark. And he also has an academic background. It's otherwise very, very different because it's B2B and started out much bigger than we are. But it looks like they, the climate they saw two years ago is very different to what we have now. So I'm getting all my tips from him and half of them don't work anymore. The climate is not good in the moment. Even in the hype space of creative ai, there is a lot of chicken egg problem, happening in the sense that investors wanna see. They actually want pay traction very often .They want to see some pay traction or immense numbers in weightless users or something like they wanna have proof of actual viability very early on. But it's such a new area that you are actually creating a market. So it's very hard to say where this whole journey is going because the whole, like AI is not super new. But generative AI is really something that is only a thing since like a year or so. It's very hard to say where the journey goes in the moment and, like it could all still just be overhyped. Then I would understand the need for having paid traction, but it could also be that we are just opening, creating a completely new market here, and then a little bit of trust would be nicer than having to prove things too early. Dhaval: Yeah. Yeah. Where are you in the product stage in terms of product development? Are you close to? I know you mentioned you were ready to launch in a few months. Are you close to finishing your product development? Is is that almost there? Like, if you can share that. Cause my follow on question's gonna be on, what were your top learning lessons around creating an AI generated product? What was that experience? What was the top learning lesson there? Martin Pichlmair: So I think the whole idea of finishing a product is not really how it works with software as a service anyway, but especially in this extremely fast moving area of, AI in general, but especially generative AI where new technologies come out on a sometimes weekly basis. There's a lot of competition, but there is also just a lot of speed of development. I don't think we will ever be at the moment where we say, now we are done with this. Instead, what we are, where we are trying to get is to a point where we can say, this is our 1.0 version and we hope to be there in the month actually. And, from then on we of course continue building. and one of the main challenges to my surprise actually, an

    18 min

  5. 04/03/2023

    This former Dropbox engineer built an AI Product to create stylized eCommerce product photos

    Boyang Niu is the Co-Founder of Stylized. He holds Bachelor and Master Degrees in Computer Science from the University of Pennsylvania. In today's episode, We discussed his journey and vision for the AI-powered e-commerce platform. Boyang shares the long-term vision for Stylized, which is to become an asset-first e-commerce platform, simplifying the process of setting up an online store by taking care of all abstractions, from website building to SEO. Boyang emphasizes the importance of understanding one's strengths, whether it's distribution, core ML, or UX, and iterating quickly to create a high-efficacy product. Tune in to hear Boyang's insights and experiences in building Stylized and how you can apply these lessons to your own business. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Andrew: • LinkedIn: https://www.linkedin.com/in/boyang-niu/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt  Transcript:- Dhaval: This former Dropbox engineer built an AI product to create stylized professional product photos for people running e-commerce shops. Boyang is a founder of Stylized ai. He Figured out a way to leverage depth extraction, AI and 3D rendering to empower e-commerce sellers to transform their phone photos into professional assets for everyday use. One of the biggest lessons I gleaned from this conversation is how he approaches product development by focusing on areas of growth and applying the user mindset to product creation. Hey, Boyang , talk to us. Tell us about your journey. How did you come to, identify this, space? And, tell us a little bit about you first and then we can talk about the product. Boyang: Yeah, absolutely. Great to be here. Dhaval I joined Hive. So Hive was a social media company back in 2015, building like a Twitter clone. We pivoted hard to enterprise computer vision SaaS. And so that's where I first like learned about ImageNet, um, did all this AI stuff built like a trading pipeline and all of this stuff. And then after that, worked in some productivity tools at Dropbox, worked at e-commerce at Square, and then for the latest venture, I like put all those things together. And really it's about targeting the market that I care about, which is e-commerce sellers, which is a huge market, right? You get a lot of customer iterations, a lot of customers to go after. You don't have to really be scared to approach any particular one. And that's a great feeling when you're just getting off the ground, right? Because you need that iteration cycle. And so we, we. Sort of putted around me and my co-founder, looking for ideas that really resonated. And one of the things was like, oh, people want to take photos, right? People, when they sell things, they need photos of the thing. that's where buyer decisions are made, and they pay like 35, $50 per photo professionally for these images that they're putting on Shopify. And so people are coming up to us, they're being like, oh I waited like six weeks for this photo set. For, 250 photos, it cost me 10 K or 5k, whatever. And we're like oh this is interesting, right? Because, there's this new image stuff going on and maybe we could really leverage that, to make this workflow better.So that's really where we came up with the idea for, what is now Stylized, Stylized.Ai. What we're building is professional product photos, for people running e-commerce shops in under 30 second Dhaval: oh wow. So tell us a little bit about where your product is at this stage and have you launched, is it in the pre-launch stage? Is it in the wait list, stage, et cetera, et cetera. Boyang: Yep. We have launched, we are soft rolling out a launch with this is a prosumer product, right? And so we're really concentrated on the B2C motion of go to market. We're doing like organic seo. we're running a bit of ads on the side. And so this is all just to build up , a brand name and also to get really fast iteration on what the product surface is. So we have soft launch. We have about 200 customers right now. That's growing probably at a rate of, I would say, like 15 to 20 per day. Which is pretty good, right? It's only been like, I think since we opened the beta. To one and a half weeks. So we are pretty happy with that. And I think the goal for us really is to get that distribution and get in front of people into their workflows such that we get embedded. And because no one really knows, like honestly, no one really knows where the AI models are going. And so by in the next three months, someone could do some really magical stuff, right? And we want to be able to put that magic in front of customers. And to do that, you have to have a big audience. So that's our goal, right now Dhaval: That's awesome. Focusing on distribution first, that's novel. Most of the founders and product creators, they get their heads down and they start building the product and it. They spend months and months and months before even thinking of the first interaction with the customer. And as you already know, it never goes as per the plan. Right. So what is the main value proposition? What is the main customer pain point that your product solves for? Boyang: Yep. Customer pain point. I have a product. I'm trying to sell it. I have a Shopify store. I need good images. Right now my options are I get like a light box set up, which is, they can be pretty complicated, right? I need to set up a photo studio area in my house. I need to take pretty meticulous pictures. I need to then learn Photoshop and edit those just the way I want them. Or I go to a professional studio and get my photos back to me, in a couple weeks. And so that's my blocker right now. I can't get it on Shopify. So what we do is, you. An iPhone image, and as long as it's like pretty good neutral lighting, like anyone can do this. I've done this many times and I'm bad at taking photos. Right. So I do that in my product. In 30 seconds I get a virtual light box. So this is a staged, 3D rendering actually. And the technology is relatively, I wouldn't call it simple, but it pieces together a bunch of existing models, right? To render your product in 3D. And then you get to adjust whatever you want about that rendering such that you know your product is professionally lighted. You get to change all the backdrops as if you are in a product studio or in a photo studio with like different types of materials or Hey, I want this on marble or slate, or all of that stuff. But you get to do that from the comfort of your computer and the iteration time is, on the order, five seconds versus two weeks. so that's what we're going for. Dhaval:  Wonderful. Yeah. I've been a product owner for e-commerce companies and that finding good stylized photographs of your product has been the biggest game changer. Like experiencing, showing people experiencing the product has been the biggest game changer. So you're solving a real pain point. You said you are in a prosumer space. If you can unpack that a little bit, why is that presumer and not just e-commerce sellers. How do you differentiate? Boyang: Yeah. So for us, the biggest differentiation is whether we are b2b, which would be selling to e-commerce platforms. And we've talked about this as well as whether we could go to Shopify and say, Hey we have an api, or we have a third party tool that. You could purchase for your sellers to make their shops more efficient. Whether that's the route or if we want to go directly to the customers themselves. So we see that as more presumer because it's self-serve one, we're just launching to anyone. You can come in, you enter your email, um, you upload a picture and boom, it's there it's free to use. You just get these premium add-ons. And that's how you are introduced to that product at first. So we're calling it prosumer mostly because all of our customers are independent shop owners, and they really get to make the decision about their own product. Dhaval: Very cool. Yeah. So your product roadmap could be either build up your distribution, get a lot of, Prosumer e-commerce, use your product, and then become down the line, become this extension or plugin for all the e-commerce outlets that are out there. Is that something you're thinking of? Boyang: Yes. I guess I won't go into too much detail, but we do have a Shopify extension that's coming out soon. As I said, we're focused really on hitting that distribution and just nailing it, getting as many customers as possible. And one of the, one of the benefits here is like we're solving one. Very individual problem, right? So it's, Hey, I need a photo. There's a very clear input, like I take a photo, there's a very clear output, I get a better photo back, right? That takes 30 seconds, 15 seconds, whatever it might be. We're solving that pain point. So it's very easy to get in front of people and say, Hey, look. This is what you're getting from us. and it's easy to onboard. And then from there, I think the strategy here is if we get many customers, we can start building up, catalog extensions. We can say, Hey, put your entire store catalog with us, like we will optimize it. Or you get better photos through all of your store. And then we can really start to leverage all of the newer AI things that we see coming out in the future. So if one day there are, very good AI models for just creating catalog webpages, we could leverage that. We could then let you know our customers create their own webpages directly from our surface. And that is really the expansion route that we're foreseeing. It's like an asset first, website. Dhaval: Very cool. So we have talked about your journey. We have talked about your product. We have talked about your potential future roadmap. What I would love to dive into now is unpacking the product stack a little bit for people, for product managers or product owners who want to either make an AI pr

    19 min

  6. 03/31/2023

    How founder this build a website copy and image generation plugin

    Andrew Palmer is the Co-Founder and CEO at Bertha Ai. Single Dad of a Twenties daughter He love to travel, play golf and attend WordCamps around the world. He love supporting Plugin and theme developers across the globe as it helps them get a profile and earn a living from their knowledge of WordPress, AI Content, PHP, Laravel and JavaScript. In today's episode, We discusses his journey and the development of Bertha AI, an application layer built on top of OpenAI's GPT-3. Bertha AI is designed to help website owners create and manage content efficiently. Andrew shares his advice for new entrepreneurs interested in creating AI products to start by fine-tuning their prompts and understanding what they want to achieve with AI. Tune in to hear Andrew's insights and experiences in building Bertha Ai and how you can apply these lessons to your own business. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Andrew: • LinkedIn: https://www.linkedin.com/in/andrewpalmer/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt In this episode, we cover: [00:00] Introduction to Andrew Palmer and Bertha AI [00:04:20] How Bertha AI uses GPT-3 for content creation [00:07:47] Andrew's entrepreneurial journey [00:13:58] Advice for those looking to create AI products using GPT-3 [00:17:17] Staying motivated as an entrepreneur [00:19:31] The future of Bertha AI and content industry Transcript:- Dhaval: Hey, Andrew, thank you for joining the call. I would love to hear from you on what your product is, like who does it serve, and what are the things that it solves for? Andrew: Well, Bertha.Ai solves a multitude of problems. In WordPress, for instance, we have web developers dealing with clients that really just can't get write together own content because they're not concentrating on what they should say on a website. They're concentrating on what they should say to their direct clients. They're speaking to every day on the phone or in day-to-day meetings, sales meetings, networking, that type of thing. So WordPress has a content gathering issue with Bertha.ai, when you are developing a website, the developer and all the client can use Bertha.ai to create interesting content around what the services, the particular website client provides. It can also help them write blog posts relevant to their particular industry. It can basically help you get the right words out there at the right time and much, much faster. So you're going to increase your productivity as a web developer and also if you're a client building your own website, you are going to be able to increase your productivity and get more ideas about what people actually want you to talk about within your services. So that's what Bertha.ai is all about really. Dhaval: Wonderful. Is that a content management system or is that a plugin for Wordpress? Andrew: So Bertha.ai is a plugin for WordPress, which is an application layer built on OpenAI, which everybody's hearing about the moment, probably one of the best viral campaigns out there with GPTchat. With Bertha.ai, we've got a number of modules in there that help you get your unique proposition together. You can write a full on about page. As of this week, which is January, 11th of January, so coming soon is GPTChat to Bertha.ai as well. And as of by the end of January, we'll have a Google extension out there as well. So you'll be able to use Bertha.ai everywhere. And the whole point of being able to use it everywhere is that not every website is built using WordPress. So you'll be able to use it in Shopify, Wix, those kind of proprietary website building tools. Dhaval: Wonderful. So you just answered my question that you are expanding for being a word, from being a WordPress only product to more of a open access for all types of content management systems. Andrew: Yeah, using it everywhere. So when, if you're in your Shopify site, you'll be able to just invoke Bertha .ai and write fantastic product descriptions or enhance your product descriptions. You'll also be able to get page content that is relevant to your users. So you'll, you'll ask one line question and Bertha.ai will be able to give you the answer to that question. And then you can copy and paste that, edit that to make it more human-like if you like. And then you can post that in your Shopify website or your Wix website or Squarespace, whatever you're using. Dhaval: That's wonderful idea.Tell us a little bit about where you are in your journey. When did you start first? How many customers you have? If you can share revenue. What is the split between developers versus end clients? From a business point of view, tell us or give us a few clarity on where you are in your journey. Andrew: Where we are is we're nearly a year down the line or just over a year down the line. Actually, we launched in September 2021. So, just the MVP version, and we launched that for basic WordPress plugin. So we're a year down the line. So we're way down the line. We've made the plugin faster, more accessible, easier to install. Getting the customer journey right is very important when you are building a plugin that's got a lot of things in it. There's also a learning curve as well. So we produced in just under a month, we produced 54 learning videos which are on our YouTube channel, which take you through every single module. With the hype around GPT-3, obviously lots and lots of people, you've got a million users in under three weeks, I think on GPT-3. So people are understanding how to use AI, how to ask the right questions, and obviously with images, image generation as well. So Bertha AI put image generation in there about a month ago. And that's flying. People are really kind of intrigued about how they can create unique images for their blog posts, from CVD products to computer products to any kind of product for florist, interior design, kitchen design. You can really get some great imagery there. If you want a modern kitchen design designer or a modern interior design, just ask Bertha create an image and it'll produce that image of a beautiful laid out sitting room or a house with chandeliers or whatever you like. So the point is that we came from the WordPress design or website design business. So we understand how people want to build websites. It's kind of a generic way to do that. And with the developments that we've had, we've got something like around 10,000 registered users. Some of them are website owners. In fact, I had a Zoom call with a guy today who's a website owner. They're not a developer or anything. And he wanted to use Bertha AI to better describe his products to his wider market. In fact, most of the meetings I've had with Bertha AI I love doing one on ones with our clients. And they've been with people that aren't web developers. They're the website owners. And that's the people that we really like to target. Because as I said, website owners have a real problem getting content together and writing blog posts. So Bertha helps you with all that content. But revenue terms, we're okay. We're fine. It's in profit. It's running itself. It's quite nice, we're able to expand. Development team based in UK is upon part of the development team as well. And the majority of the team are based in Kolkata in India. And they do a great job. Dhaval: So then quick question. You mentioned something very interesting. There are a couple things you mentioned. I want to glean them out. One is the customer journey. The audience for this podcast are people who are product creators and they're specifically interested in either adding AI to their existing product or they want to create an AI product. With what you just described about customer journey, what was that experience like for you? Like, understand the customer journey for your website owners. And how did you productize that? If you can share a little bit about that. Andrew: Well, it's still happening. with the WordPress plugin, we are lucky it's on the repository and you can install that directly from your WordPress dashboard. With the extension, that's going to be a harder task making sure that customer journey is OK to actually install it in a Chrome extension. We're talking about millions of users that maybe don't know how to install a crime extension from scratch because it's not going to be on the Chrome extension store for a while. It's just going to be downloaded. Once you have an account within Bertha, you'll be able to download the Chrome extension and install that. So we're building education around that about how to install a Chrome extension and maybe make it not so difficult for people to understand that it's quite easy. It takes about 30 seconds to install the Bertha extension. So it's about education, but it's also about what we want as a customer journey. There are so many extensions out there. There's so many plugins. There's so many SaaS products out there that involve a learning curve. And what we've learned is that people don't actually want to learn. So what we have to do is almost automate that process . Dhaval: I'm one of them. Andrew: Yeah, exactly. You don"t actually want to learn how to drive a car or drive a new car, let's say. So if you've got a new car and it's great, but the indicator arm is on the other side, you forget, don't you? You put your windscreen wipers on. So, There's a learning curve around everything if I really simplify it. But at the end of the day, it's up to us as product developers to make sure the customer journey is as seamless as possible to immediate use. And that's where we're having difficulty. And we've had difficulty, but we're getting better and better by that every single day. Dhaval: Now, you were a website agency website development or marketing agency, and that's how you identified this content problem that your customers were having. And that's how you

    22 min

  7. 03/30/2023

    This founder built an AI product to replace your employer with AI.

    James Clift is the Founder at Durable. He is the former Founder at KarmaHire, WorkStory. VisualCV and Holopod. He building businesses since 2005. In today's episode, We discusses the benefits of large language models (LLMs) like GPT-3, emphasizing their ability to generate human-like text that can be used for a wide range of applications, including content creation for websites. He also emphasizes the significance of having a data-driven approach to fine-tune the models and make them more effective for different business categories. James shares his experience of finding partners with AI expertise, highlighting the importance of networking and being involved in communities like South Park Commons. He believes in sharing work and attracting like-minded individuals to join the venture. Tune in to hear James's insights and experiences in building Durable and how you can apply these lessons to your own business. Find the full transcript at: https://www.aiproductcreators.com/ Where to find James Clift: • LinkedIn: https://www.linkedin.com/in/jclift/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt In this episode, we cover: 00:00:00 - Introduction 00:03:50 - The story behind Durable and its AI-powered website building platform 00:07:22 - The importance of user feedback in AI model training 00:10:49 - Fine-tuning and prompt engineering for LLMs 00:11:22 - Best practices for partnering with technical teams 00:12:27 - Durable's growth, funding, and team size 00:13:20 - Remote work culture at Durable 00:13:49 - Finding AI expertise and attracting technical partners 00:15:16 - The value of sharing your work and attracting like-minded people 00:16:20 - The future vision for Durable Transcript:- Dhaval: This founder built an AI product to replaces your employer with ai. Yes. You heard that right? Your employer, James, is our guest in today's show, he shares his learning on how he built a product in a hyper-competitive market space. James Clift is a founder of durable. Durable, makes owning a business easier than having a job. He's a former founder of KarmaHire WorkStory, VisualCV and Holopod. He has been building businesses since 2005. Welcome to the call. James, tell us about your product. James Clift: Awesome. Yeah, so Durable is the fastest way to build a website on the internet. In three clicks, in 30 seconds, you can generate a business website using ai. And not only can you make a website, we've got the rest of the stack as well to operate a business, we've got a CRM, an invoicing tool a financial account. So essentially everything you need to start and grow your business in just a few click. . Dhaval: Wow, that's very powerful vision where do you sit in your market space? are you serving a specific customer segment? James Clift: Yeah, so we're totally focused on solo operators, so anyone that runs a solo business. So primarily those are service-based companies, so everything from a web designer, marketing contractor, copywriter to more traditional physical service companies like Lawn care, home Services. Plumbing contractors, skilled trades. So essentially anything where you're trading your hours for dollars or your hours for projects we're a great fit for if you're selling goods on the internet or you have a brick and mortar store, we're not the best solution there, but solo service-based companies is our primary market right now. Dhaval: How did you differentiate yourself in this crowded market space? I believe it may be high competition market space. But You have, you seem to have found reasonable amount of success based on what I have seen about you online. How did you create that differentiation for your product? James Clift: Yeah, I think there's a lot of ways to look at markets like most markets are very large on the internet, and for us it was a few things. So one is bundling, so providing all the tools you need to run your business under one login. So that was a big value add from the start. So you don't have to learn five different tools. You don't have to pay for five different subscriptions or 10 different subscriptions. It's everything you need under one platform. And then the other piece is what can you actually do 10 times better than everybody else? And for us it's the speed of actually getting a website. Out to market. So instead of taking, typically it's weeks to get a website live, if you're really good it's days. If you're really, really good, it's hours. We actually do that in minutes. So there's this order of magnitude that makes that thing faster, or that business process faster. And it actually unlocks a lot of creativity, a lot of, just makes it more fun and playful, not stressful. And I think you open up these brand new markets by just anytime there's an order of magnitude step change. Something's 10 times faster, 10 times better, 10 times more intelligent. That creates these huge opportunities. And I think AI as a platform is definitely one of them that we're seeing. yeah, I think customers are super excited about the speed, the simplicity, and then the bundling aspect of the platform as well. Dhaval:  Very interesting. So you brought this up, AI and this particular discussion and discussions like. That I host are focused on people who are either interested in creating an AI product or infusing AI in their existing product. Tell us about your infusion of AI into your product. When did you decide that, was it AI first from the ground up? And if it wasn't, when did you decide to bring AI into the user? James Clift: Yeah, I think, so I've ran SaaS companies for a while now, so probably about 15 years. And, it's always, I mean, the goal of any software company is how do you make processes easier and make your products easier to use? So, the long-term vision of us and AI is really, How do we replace your employer with AI and just let you focus on your core competency. So that's your skill, right? So a lot of the time if you have a service job, you have an hourly rate that you're then getting marked up for by your employer. So, in a perfect world, you just meet that reach, that market demand. So, hey, you're a lawyer making, I don't know, call it 500 bucks an hour that your employer charges you out. You're making 200 bucks an hour. So that's a market opportunity for you to go independent and do your own thing. But what the law firm brings to you is a brand customers, some back office services. So the way we're thinking about that is what can we actually replace? And I think brand is changing a lot. Like the brand matters less, the individual matters more, and the back office piece can be solved with technology. So essentially . Everything can be automated except for the thing you're really good at, and that's really how we're thinking about ai. So from a product standpoint, we built the platform first, and then we built the AI on top of the platform because we've got a lot of features and it was always this idea of, how do you make those features easier to use, more accessible, more interesting, and just more intelligent. So the, our customers can just focus on what they're good at. so that's always been part of the strategy. Definitely it's accelerated in the last few months here with all these new technologies and APIs and libraries that have come out, and been super incredible and are moving really quickly. So definitely, the primary part of the strategy moving forward as well. Dhaval:  One thing, one thing that I always hear from other product creators in the space is about finding the balance between building on top of existing AI capabilities. That other companies have created versus building your own AI capabilities? Where do you draw that line in your product? James Clift: Yeah, I think it really depends on what kind of company you want to be. Are you really good at marketing? Can you repackage these libraries and build a good user experiences around them? And you can accelerate really quickly? Are you deep technologists? in that case then you should build the underlying infrastructure layer. For us, I think the advantage, and I think. These core models are really, really powerful, but I think you have to train them on your own data sets. Otherwise you're gonna lose your competitive advantage really quickly. So if you're just re-skinning chatGPT and it's a slightly different user experience, but the same data, That's gonna be a race to the bottom pretty quickly, because everyone can do that. But if you have a user that is unique that you can build data sets around, then you can train these models to be more effective. So we're doing both, we're using the existing models, but we're also training them. Pretty specifically around our category of customers. If you think about a solo business owner, there's a set of activities that you need to do. Even from a marketing standpoint. It's okay, you've got your website. How do you optimize your seo? How do you create your ads? How do you create your marketing copy, your newsletters? Once you have customers in, like, where do you get more customers from? How do you measure your channels that are effective? When you send invoices, what is the value of that invoice? How does that tie to your accounting system and your customer database? So there's just a lot of things that we can actually build more proprietary, unique data sets around and workflows and processes that we can optimize. So I think as long as you own that customer journey and lifecycle, then you have the ability to train your model and make it better. But yeah, if you're just re-skinning an API, I think. There's some that will do well. So like re-skin the API in the category. I think one or two of those will succeed in every category because it is great tech. And if you're good at marketing and acquiring customers, there's opportunity there. But if you're the, I don't know, the 20th company to

    19 min

  8. 03/29/2023

    This founder built an AI Writing Product that serves 4 million customers and got acquired in 2 years.

    Abhi Godara is the Founder & CEO Rytr. He is also the Founder & CEO at HelpTap. Rytr is an AI writing assistant that helps you create high-quality content, in just a few seconds, at a fraction of the cost! In today's episode, We discusses the initial stages of his startup, where they utilized organic channels like LinkedIn, Facebook, and Reddit for marketing. He also discusses acquiring training data and recommends strategies depending on the domain, mentioning that GPT can work with a limited number of examples. Abhi highlights the importance of user experience in differentiating his product from competitors. Tune in to hear Abhi's insights and experiences in building Latitude and how you can apply these lessons to your own business. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Abhi Godara: • LinkedIn: https://www.linkedin.com/in/abhimanyugodara/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt In this episode, we cover: 00:00:00 - Introduction 00:03:25 - Abhi's motivation for building an AI copywriting tool 00:05:15 - Strategies for acquiring the first thousand customers 00:09:40 - Differentiating the product in a competitive market 00:12:21 - Acquiring and using training data for AI models 00:13:50 - The story behind the acquisition by Copysmith 00:15:08 - The future of AI in content creation and advice for AI creators Transcript:- Dhaval: This founder built an AI writing product that serves 4 million customers, and it got acquired in two years from Founding Date in this episode, we discuss his product development approach that differentiates his Gen AI writing product from the plethora of other gen AI writing products in the market space. We discuss his product differentiation strategy, his training, data gathering approach, and how he got his company acquired. Today my guest is Abhi Godara. He's the founder and CEO of Rytr and AI writing assistant that helps you create high quality content in just a few seconds at a fraction of the cost. Welcome to the show, Abhi tell us about your product. Where are you at with it? what's the four 11? Abhi: Right thanks Dhaval for having me. so I'm founder and CEO of Rytr one of the largest and probably the first one in the market AI writing platform. We have been there since last couple of years now. now we are serving close to 4 million customers all over the world with with close to perfect ratings pretty much on all the platforms. So it's been an amazing journey in terms of how, the platform has scaled which allows a lot of these content creators. Marketers And professionals to create really high quality copies across a range of use cases, purely through ai. So things like email writing, blog writing product description ads, you name it. Everything can be generated through our platform. Dhaval: When did you found the company? Abhi: So this was back in 2021 actually when we started working on this. although I've been in the AI space for a long time. but this idea took off only when OpenAI came to life back in 2020. So I was following that closely. And then when GPT 2 and then GPT 3 came out, and we bounced on that seemed like a great opportunity to build something like this and just to give you some background to that. Again I've been an entrepreneur for most of my career. And, when, one thing I've always found that content creation is a pain, especially when you're a small team just starting it's a fact that many startups and professionals fail because they do not possess the effective marketing and copywriting skills. While dabbling with GPT 3 on another sort of chat bot project, I realized the potential of this technology and the market it could address. And at that time we looked around and evaluated existing platforms and found the experience a bit frustrating. And decided, okay, let's give the market what it deserve. And that's how the AI writing tool was born. I think we were probably in the first six months of this technology when it came out. We launched this and yeah there is no looking back since then. From zero to almost 5 million customers now. Dhaval: Wow. 5 million customers in less than two years. Did you bootstrap this? Was this venture funded? Tell us a little bit about the financial side of the business, if you may. Abhi: Yes, absolutely. So the funny story is , it was completely bootstrap zero external financing or capital reached. We had a acquisition as well, last year now part of a bigger umbrella company called copysmith. And yeah, it was always a small team and even. As of today, we are just four people. It's a very, very small lean team. And for the first six to, I think nine months, it was just two of us, me and my co-founder, and we were just doing pretty much everything. So yeah, it's been a lean journey completely bootstrapped and even as of today we are a very small team that is focused on product and high quality customer support. Dhaval: Okay. We'll switch to gears a little bit on. Where did the AI kick in for your customer experience? Customer journey? How did you make that decision that in this point of customer journey will be infusing ai? What was that decision making process like? Abhi: Yeah, so I think the whole product itself was like, The foundation was ai, right? When GPT 3 came out, like it, it allowed people to create all kind of content and copies by just giving some examples or you can see, training data so when I played around with the technology, I could see the potential. Wow. What if I can turn it into a delightful experience for end users who can create all kinds of copies. So we did a lot of our own training data in terms of the different kind of copies that people would like to generate we trained the, the underlying sort of models which were provided to us by GPT 3 OpenAI. And then yeah, so the whole product was basically built on that technology from day one. AI was always there. It is an AI writing assistant, it's natural that AI is there. So yeah, so it was always AI first product, AI first pocket you can say. And when we launched, this was just heating up, this space was like just coming to life, I think now. AI and GPT 3 chatGPT all over the news, but maybe a couple of years back it was just a very sort of em embroiling, technology. Not many people knew about it. So yeah, but we decided, well, something like this can really make a difference. So that's how I think we bounced on it. Dhaval: Yeah. You mentioned something about you fine tuned the models that you got from open AI. For new and aspiring product creators who may or may not have deep expertise in ai, is that a preferred route? Is it easy to fine tune existing foundational large language models that you get from OpenAI? If there are any tools you could, you would share with? Abhi: I mean it to be honest with you, yeah, I think it's, uh, they've made it very easy. So it's not even a non-technical person can feed in some examples and have the AI. Produce content, which is of high quality and aligned with what the user is expecting. so it's not a highly technical of course you can fine tune to the extent that you can provide like thousands of examples with your own custom domain or maybe industry. And then the model would be like very, very customized to your needs. But , we didn't go that far and I don't think majority of the use cases need to go that far unless you're working with enterprises, I guess. so in our case it was, and this was like a couple of years back, now it is matured even further. So you can actually go in with chatGPT or any other such similar technology and with the zero short learning they're able to give you the output that, it's pretty decent. So, yeah. So I think even non-technical founders they're looking to get into the space. I think with some fair like industry experience, they should be able to train the underlying model, which doesn't require any sort of technical expertise. But if they're working with, I think, bigger clients and companies and enterprises, I think that's where maybe they would have to fine tune it a bit more. Dhaval: Wonderful. What was your biggest learning lesson in terms of finding the product market fair, especially with AI capabilities? When was that light bulb like, yeah this is happening. I know you are an AI first product, but uh, just in terms of okay, yeah, this is where we are starting to see the fit. What was that? How, what was the learning lesson there? Abhi: I think I, a lot of it was like market being at the right place at the right time as it's the case most of the times having been in the AI space for the last five years building like so I've been working on this AI chatbot tool for individuals and influencers. But then the tech wasn't there at that point, to create any sort of. Custom implementation, you would have to train tons of data and even then the responses wouldn't be anywhere close to what the user would expect. So having been through those struggles, and then suddenly when open AI release, GPT 2 GPT 3 I could see the remarkable differences in the output quality. And that's when like I said, I realized, okay, well this could be packaged into a much better, bigger product for a lot of these copywriter and marketeers. And to be honest with you, that that was, that seemed like the first, logical use case of this technology. Now you can think about a lot of other things, but at that time, I think content creation creative kind of copy generation was probably the first thing which would come to your mind, and that's how we got started with this. I think that's when it hit us. Hmm. This could actually do a lot of good things for small businesses and startups. Dhaval: Yeah. So for you it was more of a, just making sure that the, the idea and the insights that you had around being able. Use a large language model for the end users and being able to implement th

    17 min
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Welcome to the AI Product Creators podcast, where I interview AI product creators and innovators to learn their best practices and tips for creating successful AI products. In each episode, I'll speak with a different AI product creator to get their unique perspective on creating and launching AI products, from ideation to development to marketing and beyond. Our guests will be sharing their insights on the latest AI trends, key considerations and strategies for success, and real-world stories from their own AI product journeys. Whether you're an AI novice or a seasoned creator, this podcast will help you gain the knowledge and confidence you need to create successful AI products.

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    Dhaval Bhatt
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