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Documenting the stories of tech founders turning ideas into thriving businesses.
Marcus Papin (00:00)
Welcome to the Tech Founders Podcast. I'm your host, Marcus Papen. Today, I'm super excited to be talking to Neve Fowler from Druid Learning. Neve, welcome to the show.
Niamh (00:08)
Thanks
for having me. I'm looking forward to having a chat.
Marcus Papin (00:11)
Let's get started with a quick introduction of yourself and your company, Druid Learning.
Niamh (00:14)
Sure.
So as you mentioned, my name is Nia Fowler and I'm the CEO of Druid Learning. Druid Learning is a trend we basically from a technical perspective or an ETL solution with a generative AI I push, focused on the media publishing space. When talking to non-technical people, the kind of approach I take would be Druid Learning is a
an AI platform that takes historical content, turns it into an AI data set, and then allies journalists to use predictive content generation to scale their content. That's generally the way I kind of pose it when I'm not really talking to technical people. So yeah.
Marcus Papin (00:56)
Awesome.
And what's the current state of the business and any sort of metrics that you're comfortable sharing?
Niamh (01:01)
Yeah,
so, ⁓ Dured Learning is...
has just turned five years old. And I started the business in kind of the COVID era. And I was very much focused at that time on fixing a COVID problem. And one of the kind of one of the co founders in the business, Hillary, was basically a teacher at the time, she worked a lot with publishers, kind of generating specific books and material for publishers for them to produce commercially. And she approached me kind of
saying that like in her class, a lot of the kind of weaker students were falling behind because of the move from classroom based learning to tablet based learning. And she was kind of concerned because with kind of from her experience working with educational publishers, like one of the big issues that was being kind of.
faced, I suppose, was the fact that all of their content was not necessarily compatible to delivery on a tablet device. So at that point, kind of worked with her. was still working kind of in my own, was working professionally in a large international retailer ⁓ as a technical product manager. And sort of in my spare time developed this tool that was basically
a large content management tool that itemized and kind of flipped content into essentially individual assets for, and we created a GUI for delivery on tablet devices with accessibility options available to them, to students. So it was kind of like an odd starting to the journey, but once COVID finished, I got, kind of when I left the job,
I kind of went into sort of freelancing. I focused my time on the business. And basically from there,
got some European funding actually, some European horizon funding to put into the product to change it into what it is now. So we pivoted slightly, but really we kind of focused on large content assets. like instead of now creating a GUI for accessibility purposes on tablet devices, we now allow for a predictive content generation using the historical assets as a, as a base on which content is generated. So that's kind of a very kind of
convoluted way of saying we started out doing one thing pivoted slightly into doing something else and as of this year, we're revenue generating and we kind of have a number of international and national clients. So yeah, we're excited about where we're going. As of like kind of year to date, we have 150,000 in revenue, kind of 75,000 of that is ARR already.
So we're kind of beginning to focus on flipping our initial design partners into kind of.
basically license-based contracts. So kind of our focus for next year has been like from building the product this year into kind of selling what would be classified as an enterprise level product. But obviously we still have a bit of development to do on it. So yeah, it's going well so far. Like as of kind of this year again, we've generated or investment wise we've...
essentially accumulated a combined amount of about 750,000. We aren't actively looking for investment at the moment, but we will open it around probably at the end of Q1 next year. So yeah, that's where we are at the moment.
Marcus Papin (04:39)
Awesome. Well, congratulations on becoming revenue generating. That's a humongous milestone. so the team is yourself as the CEO, Hillary is your co-founder. And is there anyone else working with you guys or?
Niamh (04:42)
Thank you.
Yep. Yep.
Yeah, so there's
two, well kind of three people. So at the moment we have myself and Hillary who are full-time ⁓ employees. And then we have three kind of contractors, but essentially they're full-time contractors. And we are hoping to bring them in full-time into the business kind of after we raise. But we have them as contractors just from a cashflow perspective, but they're basically working 40 hours a week. So we have Jattin who is our CTO, who was actually a mentor
Marcus Papin (05:16)
You
Niamh (05:21)
on the TechSense program ⁓ and who subsequently came into the business. And then we had, we have Grace, who is our chief data scientist and we have Calvin who is the person, he's kind of our everything person. So he has great experience in setting up IT systems in third party clients. So we took him on board essentially to help us basically,
implement our solution in our clients' organizations. And when he's not doing that, he's helping our IT teams to sort of like manage the roadmap that we have. So we have also we use sort of contractors for development as well as developing ourselves internally. So he helps us manage kind of both those situations.
Marcus Papin (06:09)
Awesome. I'd like to go back for a second and talk about that initial pivot that you guys did. So you're working with schools back then and then there was the pivot kind of post COVID. Can you dive into what led you to that pivot and then also how that helped determine your current business model now?
Niamh (06:12)
Yep.
Yeah, it
was really interesting, I suppose. We were fortunate enough to be revenue generating pretty much immediately when we were obviously fixing a very specific COVID problem. And we were working not only with schools, but we were working with educational publishers. And I suppose for us, the key was that we were essentially bridging the gap between what the actual schools needed and kids needed and kind of their...
often historical archives, which were like, you know, EPUB files and, you know, PDFs and just like really what I would call rigid file types. And so when we were dealing with education publishers, we developed quite a good relationship with them because they were
very much reliant on us to helping them solve this issue. And so we use that as an opportunity to sort of really understand the not only the problem we were fixing, but some of the actual underlying issues within their businesses. ⁓ And so when COVID finished, one of the biggest things of course was, well, if you're solving a COVID problem, the problem goes away when COVID ends. So we use the revenue we had already generated to kind of pivot, begin to pivot the product.
And we had known and we had experienced through our relationships with the educational publishers, we understood sort of, I guess, what the underlying problem was. And that was obviously unstructured data that was, or unstructured files that were sitting on their databases. So when we began to sort of pivot, I had reached out to a number of what I would classify as like...
sister industries, so news outlets, media outlets, and basically kind of began to, I suppose, probe and see if this was an existing problem in those organizations too. And kind of it became quite clear that it was. And then we were fortunate enough to get onto that European Horizon program, which is called Stadium, which was a news and media focused, like accelerator. And really from there, we kind of got
And we got EU grant funding to 150,000. And we used that plus the revenue we had generated to pivot into fixing kind of the unstructured file issue in those organizations. And that was kind of, that led us on a path to get to where we are. So that pivot very much came from having built the relationships with publishers and then kind of doing it.
you know, a bit of client research at the point when we decided to push our hard-earned money into building a kind of a spike.
an adjacent product, as it were. We use a lot of the base code, but we have to change a lot of it. yeah, it was, it was a journey, but I'm really glad we did it. I mean, it, it, it meant that we were kind of not revenue generating for almost two years, which was quite scary. ⁓ But kind of we, we've managed to sort of get a product now with a good number of design partners to a point where we could potentially sell like license based agreements without potential patents, which is the hope for next year.
Marcus Papin (09:30)
Awesome. Can we walk through exactly if a journalist or a publisher comes in to work with you guys, how that relationship would start, what sort of what you guys are, what service that Drew Learning is performing and then what the output is just so we can really put together exactly how the business, what it is. Yes.
Niamh (09:36)
Yeah.
Yeah.
What it is. Yeah. Yeah. So one of
the really interesting things is that actually when we were developing the product, we realized quite quickly that we would have to, if we wanted to sell into a news organization, for example, we have to provide a beneficial out push in their view. Right. So we knew that we were going to be able to create an AI ready data set in the organization so that they could then use in other AI applications, for example. But
If you talk to a journalist about that they're like, what? So we knew that we had to...
allow like whatever we did, we had to have an additional benefit. in our view, we were like, okay, well, with what we're doing, we can increase content outbush and reduce the amount of time that somebody takes to write an article. So I'll talk about a specific example with the client that we have. So a client of ours can approach us to say, look, we want to increase the number of content files, or we want to increase the number of articles we create on a daily basis, because if we're able to publish more
content, we get more commercial ad revenue. then we kind of, and the problem right now is that like, you know, a lot of our journalists will either spend ages like searching for content that we already have to use it as a starting point instead of starting, like creating an article from a blank page. Right. So when they approached us, they were like, we don't necessarily want to use generative AI, even with sort of like private versions of these like
public models. like, you know, the, there's still a lot of concern in what I would call the content industries about who owns commercially set, like who commercially owns content generated from these tools. Right. So you take open AI as an example in chat, GBT. So we said as a starting point, look,
Marcus Papin (11:17)
you
Niamh (11:37)
We'll take five years of your content and we'll actually process that and we'll label and annotate that entire, like that entire kind of five years of data or content. And what we'll do is we'll also kind of like highlight and kind of just through our, our annotation process, kind of allow you to kind of get to a deeper level of insight into what your content is and kind of like, you know, see the trends within your content, blah, blah.
And then I said that from there, one of the things that we can do is as your journalists are writing in your authoring tool, we can actually allow, you know, through a a plugin, we can allow those organized, like we can allow those journalists to start writing a content article about, I don't know, let's just say Megan Merkel. And if they start writing about Megan Merkel, about Megan Merkel on holidays, and then suddenly they see
paragraphs that are like met that kind of are grounded in the content that they've developed about mega Merkel on holidays last year. And they're able to see that mega Merkel has been on holidays twice this year, and they're able to click on that article or click on that paragraph and put it into their into their new newly created article.
And so what we're doing is we're essentially allowing the editor to still, and the journalists to still edit the content, but we're delivering them content that's based and grounded in the facts of their organization and in the style and tone of voice of that organization. And the reason we know what the style and tone of voice is, is because we've already gone through and labeled and annotated that content for five years. So that's the idea. It's sort of like, we're, like I said, it's sort of like an ETL solution that gives us a huge amount of insight.
and intelligence into the style, tone of voice, editorial perspectives, organizations. And then that flows into us allowing journalists to either create new or publishers to create new books, material and start at like 80 % or start at 50 % rather than starting with a blank page. And that's really the key. And that's why we're able to reduce production timelines. And that's why we're able to increase that. So going back to the organization that I work with, we were able to increase that content output.
daily by 33%. Because it was very simple. We just reduced the amount of time people spent creating new articles by 10 minutes a day, like by 10 minutes every article. it was quite, you know, quite like it seems quite like basic, but actually, you know, in comparison to competitors, we're able to, we are able to deliver high quality material because we've already done the ETL process.
Marcus Papin (14:16)
That's very interesting. And the journalists access it through a plugin on the website where they can scan the database essentially.
Niamh (14:21)
Yeah, so it actually depends on the tier that they've
opted into. ⁓ if you're an enterprise client, we offer that solution. And if you're a client with like not very many articles or files, or we actually have an interface that people can use to just upload maybe like 20 files themselves, and then they can go through the same process, but that's done through our user interface. And so it's very much like dependent on the, the tier at which they're like
But yeah, for the bigger organizations, it's like, because a lot of authoring tools will either be custom built by the organization or will be sort of like, you know, a WordPress type situation. So it's kind of what we needed was the ability to sort of like the NAPI just like deliver content in whatever format or whatever application they were using. Yeah.
Marcus Papin (15:16)
Very interesting. I have a friend who writes articles for a crypto news publication and it makes the connection right now because he gets paid on the amount of articles he produces a day. So his job is write four or five articles a day. And for somebody who's dabbled in blogging before, that seems like an insane amount of content to produce in a day.
Niamh (15:20)
How cool.
Yep.
Absolutely, every day. Oof, yeah.
Marcus Papin (15:38)
Yeah, I can, using that as a reference and how he's talked to me about his job, I could make the connection. ⁓ okay. By having that data easily accessible, especially referencing, I would assume since it's the organization's previous articles as well, they trust those sources implicitly because...
Niamh (15:45)
Yeah. Yeah.
actually.
Exactly. And then what we do is
we allow them to see where that information is coming from. So if they want to fact check themselves, they can. ⁓ So that's the idea. And then also the reason why they seem to be engaged with us is because they like the fact that it's their content. if you think about the New York Times, they're publishing hundreds of articles every day. You multiply that by a factor of 50 years and you're dealing with some
serious amount of data. it's sort of like, or content files, which are waiting to be turned into data. So it's like, you know, so it's quite an interesting concept. And especially in what I would classify as content heavy industries. So not just media, not just publishing, not just newsreel. You then think about things like, ⁓ consultancy company, like management consultants, marketing agencies, type of thing where, you know, a lot of their content is that commercial
property and proprietary property. And I think that's where, you know, we see our fish. It's like dealing with those companies who are still making a lot of money from their content. But how we can essentially build an efficiency into that is really the key and how we build an efficiency is by allowing them to rely on their historical stuff that has gone before, plus that human oversight and push that will allow them to create something new as well.
Marcus Papin (17:23)
It seems like if I'm working for a news organization, being able to access data faster is a no-brainer because it allows me to do my job faster. But the world isn't as black and white. So what sort of pushback have you heard as you're going into these conversations and as you're pitching this to potential customers? ⁓
Niamh (17:28)
Yeah. Yeah.
we tend
to like this, it depends on the organization. We're also working with the, ⁓ we're working with a large research organization, like specializing in cancer research actually at the moment. And part of it is that the output of that organization is research papers and they have huge amounts of data and they have huge amount of papers and they have no standardization across their labels. They have no standardization across their hierarchies or tags.
And the pushback we got from them was like, well, how can you standardize the approach? And I'm like, well, you can always standardize the You can always. And it's not that you have to standardize, but you have to at least structure the way that you label, right? So I will annotate at least.
And so what we did was kind of with them, we kind of said, okay, well, let's workshop this, right? You think that there's no way that we can standardize? Okay, cool. Let's workshop. And then we did, we went into a three hour workshop and suddenly, you know, what do you know? It's like, suddenly there is a study thing that's like, you know, present across all of these different files. So
It was kind of, it's, one of those things where sometimes people just think it's too much of a task. And like for us, we automate the whole process, right? So we, we automate the annotating, labeling and vectorizing. So what we actually do is we say, okay, well, you know, your content the best, right? You know, if, you don't have specific.
you know, if you don't have specific things you want to label, give us the broad topics. Like for example, with that organization I mentioned, you know, they had, they had something where, you know, they had like years and years and years of historical, very local content. And one of the things that they said was like, I don't know what we can do. And I was like, just, you know, let's, let's talk it through. And it became quite quick, very like quite apparent quite quickly that
You know, their articles were broken out into like lifestyle pieces, sports pieces, blah. And so at least you have been a parameter and then that helps a lot anyway. And then you can, because our tool assesses the actual content of the files as well as like in order to label. we were able to sort of like not only create some sort of framework and structure against which they could like search because we have some magic search, but like we were able to, we were able to offer them. Like at least sort of a way against which they should see their own
content. like this is the thing, like a lot of these organizations don't really know what they have in their content files. Like they don't like they have, you know,
Like some of the educational publishers we work with, like they've been around for hundreds of years and they're like, well, we don't really know. Like we have these OCR files and like blah, blah. And you're just like, okay, we'll just have them over to us and you'll see what you, you'll see what you have. And then often they find that actually the idea of reusing material plus an overlay of generative AI focus on a particular audience. then, you know, Bob's your uncle. You don't have to, you're not necessarily again, creating from scratch, but
also relying and reusing some of the content they've done before. So yeah, it's a really interesting, it's a really interesting area.
And like kind of my exposure to it was partly because of that original, the starting point of the product. But also I worked in Amazon in their digital media area. So I was like looking after it sort of Prime music, Prime video, that type of thing. So I was aware that this kind of content stuff is like a bit of a mess. And it was kind of, yeah, it's sort of when you think about it, there was really no need for structured
archival content. Whereas now in the era of AI, there's a huge need to kind of transform that into usable data, which can obviously, you know, impact your bottom line.
Marcus Papin (21:25)
Yeah, I think in comparison to the like open AI and those models where they've scraped the internet, they do have a lot of data there, but it's vastly different than what we're talking about here because that's publicly available. And I'm sure these companies, one, their data is not publicly available, two, they don't want it to be, but they need a way to organize that. And as you mentioned, if they've been around for...
Niamh (21:36)
Yeah. Yeah.
Yeah.
Marcus Papin (21:50)
20, 30, 100 years. There's a whole bunch of data there. That's kind of almost like a gold mine that's sitting there that's been built up over a long amount of time.
Niamh (21:57)
Yeah, yeah. And that's in my pitch,
I kind of say, look, it is a very sitting on a gold mine of assets. And, you know, in the future, and this is something where, you know, obviously, we don't, we don't own or claim any ownership over their files or content or whatever. And in the future, when the data wall is ultimately hit, you know, if they have, you know, archives from the 50s, 60s, you know, that are
potentially training data that can be kind of packaged and sold to large organizations. So like the kind of, you LLM ⁓ operators. So, you know, there's a lot of potential commercial relevancy to what we're doing in those organizations. And depending on the type of organization and the resources in that organization, people are already thinking about this, or in some cases they aren't because, you know, just the maturity around AI is so vastly different in different organizations.
Marcus Papin (22:51)
Yeah. In terms of the technical complexity of Apple actually implementing this within the organization, obviously there's one in the example that you gave, which is explaining to the organization, Hey, we can structure the data. And that's a human to human objection. But in terms of technically being able to get this data that could vary immensely, depending on the amount of content that organization has, what are some of the challenges that have you found there in order to get that to a place where
Niamh (23:03)
Yeah.
Marcus Papin (23:21)
people within the organization could use it. ⁓
Niamh (23:23)
Yeah. ⁓
to be honest, we have the biggest issues we've come across is when something is semi-structured. So what I mean by that is we were, we were dealing with a quite a large database of content recently and the, like they had a, they had some decent metadata around their assets, but it was all wildly different. Right? So essentially what, what we ended up having to do was like,
almost like take a step further back and kind of say, okay, well, what do they have? What do we have to supplement? It was like that gap analysis. Like it was essentially a gap analysis that we had to do. And, you know, normally this process is quite straightforward and streamlined, right? Like we just go in, often we're dealing with unstructured, completely unstructured data. And it's actually easier for us to deal with that. Whereas when, you know, the issue is when it is semi-structured and you're kind of then trying to
like identify the gaps so that you can fill it in. And that's really the main, the main challenge that we've kind of encountered. And I think it's not unusual. And I don't think we're, I think we're going to encounter it again. So it was a good challenge for us to sort of work out a good process around kind of that situation. So if we receive files that have some metadata attached to it, and then we have to go in and annotate and label and add to the metadata. We, we have to, we kind of essentially.
you had to implement a step that says review what there is match against the what is expected and then, you know, fill in the gap and look up this resource if you are. So it's, it was kind of like we did have to have sort of, we had to build in certain logic to, deal with that essentially to deal with that.
Marcus Papin (25:06)
How do you work with businesses kind of working backwards from, have all this data, we've gone through the process of being able or understanding at least a plan of how we can structure it. How do you then tie that back to presenting the data to these businesses in the best way that serves them?
Niamh (25:15)
Yeah. Yeah.
Yeah, so we actually
have like, I mean, one of the easiest things that we did was we developed an interface where people could just search across their assets and like it was literally a search bar and then they could see all of their info. was like, you know, when we were able to say, well, look, this is all your stuff. Like it's, you know, we've, we've already annotated, we've already labeled it. Like you go in and have a look and you know, one of the people that were chasing at moment is
like they wanted to potentially use us to ⁓ enable deep research into just to facilitate documentary, I put, or documentary filmmaking.
and one of the biggest things that we were going to do was to kind of say, okay, well, if you want us to do that, you know, you have to work with us because right now the interface very like it's, it's so easy. It's just like the search bar and you know, it's, and then you crawl back everything, or you can get a file, audio file, video, whatever podcast, whatever it might be. so it's very like that's how we're, and, when, it's useful for us because we can immediately show them the value we're adding.
⁓ but really for them kind of specifically the organizations we're targeting, it's the idea of the content type, but that's really the, that's really the kind of clincher. ⁓ but yeah, that's sort of the, you know, the immediate benefit is something and it's good for us because as we're implementing, we're able to kind of immediately deliver something that's adding value. ⁓ because it's, often takes a bit of time to
work out what their authoring tools are, like ensure that our ABI is able to, you you know, it's just, and in some cases there are like custom.
built tools. it's, you kind of have to, it, some cases, implementation, maybe like maybe two months or one month or whatever, depending on the tools that they're using. So for us, it's good to be able to show immediate benefit after we process those files and we say, Hey, why don't you go and have a look and you know, mess around. I'm like, you know, in another world outside of AI, that would have just been a data asset management tool. ⁓ but
You know, it's it's and it still is, but we don't necessarily call it that. You know, they're able to go in search across their assets, search across their archives, semantic search stuff. You know, it's just like, you know, ⁓ you know, Lee and Blue Coat or whatever, and they're able to go whatever they want back. So it's it's good. It's like I think it's beneficial for us, especially as we are earlier in our.
Our growth stage, so working with larger organizations to be able to say that we've delivered something within a week, it adds to our credibility.
Marcus Papin (28:05)
You mentioned to me before the show you guys recently had a new product update. Do you want to dive into that and what that was?
Niamh (28:09)
Yeah.
Yeah. So we actually, ⁓ I mentioned to you before that we had, we have the capability now of like, if a company has only like 20 files or like maybe 14 files, we're actually able to offer them the same solution in terms of like content generation to like, you know, 80 % and then they're able to kind of go off and develop new stuff. So we were, what we wanted to do was focus on
⁓ allowing the same benefits for smaller organizations. And one of our, reason why it was because, you know, a lot of smaller companies had approached us and we wanted to offer them a kind of a solution that was less expensive, but still had the same output and benefit. And I think that was kind of, that was the latest update. It was like kind of, we had been working on it for a while, but we were able to actually make it work to a good high quality degree. So we, kind of released that and we now have.
client who's working and using it like not paying a lot of money to us but we're happy enough to get their feedback and just see what they see what they think about it so yeah so that was the it was very exciting
because like previously our con our, our generator tool was based on like, needed a huge amount of data to make it appropriate and work. Whereas like that was a big win for us that like now all we need is like a couple of files. We still do the detail processing background, but it's like, we're able to, we're able to like appropriately parse a lot of the info. Like, so the, we added in a step that they said, you know, ⁓ what topic do you want to cover? And it will be like, let's say space.
and planet earth and space and that was the stuff that we were missing now like and essentially that planet earth and space would be.
crawled basically, we'd look at all those 20 files, see what info there was about planet in space, draw that back and say, do you want to use this and generate other stuff? And if you want to generate other stuff, give us context. And so that was sort of the trick. It was sort of like we realized that actually we don't need large, huge data sets in order to allow for the same sort of approach. It just means that we would have to like scale like.
we'd have to kind of scale up and down the generative amount depending on if there was actual available information. So that was the biggest thing for us.
Marcus Papin (30:28)
Awesome, it's amazing that you have some early champions who are using the new product.
Niamh (30:32)
Yeah, yeah, hopefully,
like, long may they continue. So yeah, it's been good.
Marcus Papin (30:37)
Hahaha
Let's dive into your experience raising money and at Techstars and then the European funds. What was that experience like? Any sort of thoughts and reflections that you have after going through that process?
Niamh (30:43)
Yeah.
Yeah.
So for me, Techstars was amazing. The reason why was because as you can probably tell from my accent, ⁓ I'm not based, well, then I was not based in the US, now I am. But I needed a way to almost Americanize my business. And what I mean by that is the way you approach investment funding in Europe is very different to the way you approach investment funding in the US. The way you tell your story is very different. It's just, it's like chocolate and cheese.
And
what I needed when I did the Texas program, was the global AI Texas program and that
But they gave me was a good framework against which I could essentially Americanize my business. And I don't know the better way to phrase it, but it gave me a sense for what the, how to answer specific questions. Like, especially like you guys focus more on the ratios and things like that. Like for us in Europe, they actually talk about figures in your spreadsheet. And like, there were just differences where, unless I was actually putting like an investor situation without necessarily having done any of that tech story.
program, I would have been totally out of my depth. And I think that's where TechSize was really valuable. They helped create a narrative that worked in the US. And I think that was the biggest thing that they gave me. And then you mentioned the European program. I, that European program was what they call a Horizon funded program. So Horizon is like a
$25 billion initiative put into research, development and innovation across Europe. And I was lucky enough to get onto one of the programs and I got like, know, 150,000 in grants funding. So no equity, no nothing taken. And that really allowed us to be honest. don't think, you know, unlike the U S at that time, I wasn't really like, there wasn't really a huge amount of funding to put into businesses that were pre revenue. ⁓
and Europe isn't really a fan of putting money into pre-revenue businesses as opposed to the US where they'll just give you money. So it was kind of really useful for us because we needed money at the time in order to help us pivot that product.
Marcus Papin (32:57)
You
Niamh (33:05)
⁓ so I think for me, like investment wise so far to like, ⁓ you know, we've been, we've been successful. had some early backers in the business who stuck with us, ⁓ high networks that I had met through, ⁓ various events. ⁓ and I had, I got two high networks who have been able to kind of like contribute to funding requirements, like across the, across the three years when we weren't making, well, two years that we weren't making money.
And now kind of I mentioned that we're looking to raise at the end of Q1 and that's really going to be our first official institutional raise. excited about it partly because if we can convert some of the design partners that we have this year into longer-term ARR, I mean that makes the conversation with investors so much easier. And I think we...
We want to be in a good position where we aren't out for a long time trying to raise. Like I want to focus on growing the business commercially this year. Like I want to build us into a growth stage business, not just a startup.
And so I don't necessarily want to spend all my time raising money, even though I know that's obviously my job. But, you know, the revenue we've generated so far has been positive. And I know that we we have a pipeline of like 1.5 million. So it's like, I kind of want to focus on conversion. And but the investor like I was over some back over in Ireland just for the holidays, had some really good investor conversations, I was used to having
US investor conversations, which when I walked into having some of the recent conversations.
I've been sort of like preempting this raise and kind of building relationships with investors in anticipation for the race. yeah, it was just a different, I was like, oh, I forgot how different they were. And you know, European investors, just for anybody listening, who might be interested in the differences, the two main differences between European and US investors in my experience, I'm pre seed. And I'm raising on a pre money valuation, which
Marcus Papin (34:58)
you
Niamh (35:13)
We'll see if that's going to stand or fly next year, but we'll see. And even though I'm revenue generating, we'll see. But like the
The biggest difference is that the appetite towards risk is very different. a European investor, like certainly the ones that I've reached, I was talking to recently, European investor will want a series eight level risk associated with your business if they are investing your pre-seed, especially if they're investing pre-seed at maybe a 1 million like check size. Whereas obviously in the US, the appetite towards risk is, okay, you're showing good traction.
We believe that like we understand that you have to, you know, kind of do a bit of trial and error to get to where you need to go to be profitable and to be that kind of unicorn. So the, I suppose the level of risk associated with businesses and the assessment of risk is very different. The second thing I would say is that it's more relationship based in Europe. So
You know, I, you know, I mentioned the two, the, the investors that I have already, I met them through an event. They're European based. met them through an event and we just really hit it off and we kind of had a number of conversations and then they were investing in the business. So it's very much a.
more personal one-to-one sort of like, like your vibe situation in Europe rather than in the States. And I think there's pros and cons to that. mean, you have more grace with people that you get on with. Bush, the US investors, they know what they're doing. And when you have them on your of cap table,
they bring a certain amount of sophistication and professionalism. And so you have to sort of like raise your standard as a business owner and an entrepreneur. you have, whereas in Europe, they're kind of like, it's, you know, despite having a higher tolerance to risk ⁓ for a lower tolerance, no higher tolerance to risk. They do kind of let you just get on with it. Whereas I suppose, because I suppose the U S put bigger, bigger amounts in, they're kind of more.
dictatorial about what they want. so look, we'll see. mean, it's like for me, I'm, I'm intending when raising in the U S um, with probably like follow on support from Europe, um, particularly enterprise Ireland, which is, um, a semi state government body here. If I can raise, you know, even like 500,000, for example, um,
they'll match fund that amount. So they have some good initiatives and good incentives. So I don't think I'll be raising only in the US. I think there will be contribution from Europe.
Marcus Papin (37:58)
Awesome. It's really interesting to think of the cultural differences, even being a Canadian and now working with a US company, I can see how they structure the business, how people work, also the internal fire with some people compared to the two. It's a lot different, even from Canada to the United States.
Niamh (38:01)
Yeah.
Yeah.
Yeah, yeah, yeah, yeah. And even just like the
communication style, you know, like in New York, they're like, do you want? Like, and they're just there. But they they will give you a straight answer immediately. Whereas, you know, when you're up, you tend to get the lot of what they call the long no.
which is like, and both and if, and what, you know, it's just like, give me a straight answer. You know, it's just like, just give me a straight answer. Don't waste my time. I don't want to waste yours. You know, it's just like no one on since maybe that's because I've been living in New York for, you know, the past year and a half, but like, I'm much rather that approach and I'm much rather direct communication.
And I, because I value people's time, don't, I don't want to mess around if, know, and if there's no intention of investing or if there's no intention of buying, well, that's okay. I don't take a first thing. That's just my thought. You know, it's just, it's, yeah, it's, it's the US way is, is a lot more, I don't know, refreshing.
Marcus Papin (39:09)
Yeah, it's almost once you get over that hump of it's not personal. It's just this is how it is. It's okay. And we can move on with our lives.
Niamh (39:12)
Yeah, exactly. Yeah. Yeah.
And let's keep a relationship because you may come back around, which is what happened to one of our clients, you know. And so yeah, it is what it is.
Marcus Papin (39:26)
Awesome, Dave, as we start to wind down here, I have a question for you about reflecting on your journey as an entrepreneur up to this point. Obviously, these are things that will change if I ask you the same question a year or two from now, it will be a new answer. But if you had to look back from where you started to where you're at now, what's the number one piece of advice that you would give to your past self?
Niamh (39:36)
Yeah.
ignore the commentary. So people have a lot of opinions and they don't necessarily have insight. And so you kind of like in the initial stages, I didn't really have the confidence to back my own decision, like not decisions is the word. I didn't have the confidence to back my own gut instinct. And then I soon realized that actually I know the business the best and I also know the clients the best and like people always have opinions.
The I would say my earlier self, to my earlier self, I would say just go with your gut and you know, stand true. You know, don't listen to other people who have opinions that are coming from somewhere else, you know. So that would be the number one piece of advice.
Marcus Papin (40:31)
Awesome, Neve. And where can people go online to learn more about you and learn more about joint learning?
Niamh (40:36)
Yeah,
so if you want to learn more about me personally, my LinkedIn profile is best. It's like for slash new dollar. And if you want to learn more about your learning, it's www.ruittlearning.com. And it's if you look it up, D R U I D L E A or N I N G. That's it.
Marcus Papin (40:56)
Awesome, Nev, it's been a pleasure. Thank you so much for coming on the show.
Niamh (40:57)
Likewise, thanks for having me. It was great to chat.