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 them. That idea and follow through itself was enough for you to achieve that initial market pull. There were not a lot of hiccups in terms of like your journey itself. Once you had a great idea, you had a good execution and you just found the fit, there wasn’t a lot of like oh, we did this and then we had to pivot and we had to do this and that and that. None of that stuff. Am I getting that right?
Abhi:
I think for this idea, yes, but I wouldn’t say that it was like a smooth journey from day one of course we had to struggle going from zero to let’s say first thousand customers, that’s always the biggest challenge, right? I think that part, like first one or two months, like literally had to burst our assets to actually go and get first set of earlier adopters and users. That part was tricky and I think it had less to do with the product and more to do with the marketing side, the kind of customers personas that we are targeting. And yeah, I think we just, uh, pushed through that challenge and that’s how I think those first two or three months were so crucial in getting the foundation right. Building that initial set of audience and then from there on we didn’t look back so from product side and market side, we didn’t pivot at any point. But yes, the distribution was always challenging, in early days.
Dhaval:
Yeah. Tell us a little bit about how you went about acquiring your first thousand customers, Abhi.
Abhi:
Yeah. That’s always actually zeal for most of the startups. But, we mostly used organic channels and even today we spend actually zero on marketing, as such so you know, initially it was mostly LinkedIn, Facebook, Reddit you know, these communities and groups that we targeted made up of, like mostly copywriters. Digital marketers getting them on board as beta testers inviting them to give us feedback, giving some sort of coupons and discounts. And then we had a very strong focus on email marketing as well. Like we had set up our own funnels to convert these visitors leads into retained users and probably paying customers. And then the whole SEO was like a huge channel for us, which we had invested heavily in from early days. Again, I have a fair bit of experience on, on that side so we went all in on creating content, optimizing it for seo building quite a few I would say, authority in this space, AI writing space. So that led us to acquire a lot of organic traffic from searches. Yeah, so I think these two or three things worked really well. And of course we had partnered with a lot of these early stage deal platforms like AppSumo and all, which gave us a lot of feedback and traffic in the early days, which helped. But yeah, it’s, it was a mix of quite a few things, so I wouldn’t say that, oh, this is the only thing which worked for us and that
Dhaval:
lot of hustle. what was the differentiation for your product? How did you compete with other such AI copywriting tools? at that time.
Abhi:
That’s a great question actually. So yeah, we had a fair bit of competition, when we launched it wasn’t as noisy as it is today, but, there were still like few people who were fairly established in the space. But like I said, I think one of the reasons why we did what we did was we realized there is still a gap in terms of user experience. A lot of these tools were like very cumbersome hard to use output quality wasn’t good. They were using probably different set of models or training data. And we spoke to ourselves and said, well, we can do a better job at it. We, I have a product background and I, we really take pride in building delightful products, which really, you know make the customer happy and they should have a smile when they’re using the product. So it’s that level of I would say finesse that we want to provide in the whole, into an experience. When we embarked on it, we wanted to keep it super simple, intuitive, easy to use, and high quality outputs that people can get at a very, very, I would say, fair price point. So that is to date our value proposition the easiest most intuitive air writing platform with the most value for money for the end user. Yeah. So I think that was our differentiator. And Today that has been working very well, I would say
Dhaval:
Abhi you mentioned fair price point and high quality output, and you also mentioned training data. One question I have for you is how did you acquire your training data and what do you recommend other AI product creators do when they’re thinking of training or fine tuning the models? What is. Some of the best strategies to acquire training.
Abhi:
It depends, on the domain to be honest, that you’re working in. So like in our case, if you’re writing copies for like, emails, blogs social media, I think it’s easy to get that sort of data, right. Again, from my experience, I Have tons of, like you can say swipe files which I could use as like high quality examples of what I would expect the AI to generate and then tweak it a little. And do lots of permutations and combinations in terms of the kind of prompts you wanted to generate for the ai so I think that experience helps some domains have that knowledge freely available. And the good thing is with the GPT, you don’t need that many examples of training data. It’s, it can work with few examples as well. So we use that, we leverage that knowledge and some of our experience. But I think if you’re working in different domains, let’s say maybe finance or healthcare I think that’s where it gets a little tricky because you need like very specific, factually correct, data and which is very, very customized to a given client or an industry. I think that’s where, again, if you don’t have, let’s say, roots in that space, it would be very hard to go out and acquire that training data or knowledge base
Dhaval:
you got acquired by Copysmith. And that’s a public information. One question I have for you Abhi, is how did that acquisition happen? Like did you choose to get acquired or were you revenue positive? And the Copysmith team wanted to expand their operations. tell us a story about that acquisition.
Abhi:
Yeah, it’s interesting and amazing folks we have with the with us now. But yeah, I think it was mostly to do with how the vision and the products aligned, to be honest. And, the team at Copy Smith is incredibly good. the the CEO Shegun again, one of the very sort of visionary founders who has a very clear vision of the industry, the way the market is headed and what he wants to do. So it was more like a complimentary sort of brand and product for Coppersmith to add to portfolio. they have their own focus. We have our own focus. So it’s it’s like collective of AI. Writing or content generation products that we are building as part of this umbrella entity. So it sounded like a very natural match, for us. And again when it comes to scaling it, we would really benefit if we had somebody like coppersmith and bigger sort of people helping us and providing us the resources that we need. So yeah, so that’s how it worked out. And again, good people to have on your side. I would say. Any day.
Dhaval:
That’s awesome, man. Congratulations on that. Final question. For AI creators who are just starting on this journey. They’re just getting started. What is the future look like in your point of view? What is that?
Abhi:
Yeah that’s a great question Dhaval and to be honest with you, I think about it a lot and it’s hard to open your phone or Twitter or any other app and not see any openAI chatGPT or AI related news these days. It’s everywhere. And and my view is like it’s getting noisier and noisier. it’s a lot of every other day there’s like hundred different companies and products which are coming up so my fundamental belief is that, again, going back to the first principles, if you are somebody who wants to venture into this space, think about the fundamental needs that will never change for the end user. Whether it is the customer or if you’re talking about businesses or enterprises, it’s about those industries. So if you’re looking at it from that perspective, I feel aI will be mostly a feature in those products, existing products and services. So it’ll be an embedded kind of experience. Which most of the products will provide at some, in some shape or form. So how you can, build something which can compliment their existing products is what I would do. If I were looking at this space or if it is something which is like completely new, which doesn’t exist today. And that can only be powered by this kind of technology. Then that is another lens to look at it, right? So something like let’s say copywriting and content generation, which there was no other company which was doing this. So it made sense to create something like this but let’s say if I’m a finance company or maybe a healthcare company, I want to have AI embedded in my existing, suite of services, then I think it makes sense to do something for those enterprises instead of establishing, let’s say, standalone features or products.
Dhaval:
Thank you. Thank you so much for sharing your knowledge being here. Looking forward to staying in touch with you. Hopefully invite you back to the podcast once you build your next startup or grow this startup and share your learning lessons. Thank you, Abhi.
Abhi: Thank you. Dhaval thank you.
