How does a deep research background in science lead to developing a truly innovative digital learning platform for the enterprise? In this episode, we talk with Beza Agley, co-founder and CEO of Obrizum, a disruptive AI platform built around delivering non-linear and adaptive learning in any industry.
Episode Links
Episode Transcript
Matthew Todd
Hi. My name is Matthew Todd, and welcome to Inside the ScaleUp. This is the podcast for founders, executives in tech, looking to make an impact and learn from their peers within the tech business, we lift the lid on tech businesses, interviewing leaders and following their journey from startup to scale up and beyond covering everything from developing product market fit, funding and fundraising models to value proposition structure and growth marketing.
We learn from their journey so that you can understand how they really work, the failures, the successes, the lessons along the way, so that you can take their learnings and apply them within your own startup or scale up and join the ever growing list of high growth UK SaaS businesses.
Hey, welcome back to the podcast pleased today to be joined by Beza Agley, CEO of Obrizum Group. Great to have you here today.
Beza Agley
Thanks for having me.
Matthew Todd
Looking forward to the conversation, I think it’s gonna be an interesting one. But to kick things off, do you want to give us the kind of one liner introduction to what it is that your business does?
Beza Agley
Obrizum is an enterprise learning company. We essentially help organizations to automate many of the things that they previously had to do manually. So, we’re all about automation, adaptability, and analytics, and we’re bringing that to the corporate learning sector.
Matthew Todd
Awesome. Sounds really, really interesting. I want to get into lots more detail on exactly how that works. But before we get to that, I always like to kind of wind back a bit to that early journey, and hear the kind of the founding story as well. But, what was it that you were doing before deciding to found the company? How did that lead into what you’re doing now?
Beza Agley
I was working, actually, as a research scientist in the University of Cambridge, working in the Cambridge Stem Cell Institute, with some really exciting emerging technologies. As were my two co-founders, all working the Cambridge Stem Cell Institute. Doing weird and wonderful things from biophysics to stem cell biology to genetics, genetic engineering, and really getting to see some of the coolest inventions and discoveries before anyone else.
Matthew Todd
Fascinating. Way beyond my level of understanding. How do you get from a very technical area in the in that field to a software platform?
Beza Agley
We get a lot of a lot of questions about that, actually. It is an interesting, weird and wonderful, windy journey. But there are a lot of similarities and areas of overlap and transferable skills, shall we say, that you get from working in deep science that you can then transfer into deep tech. So an understanding of data analytics, machine learning statistics, and pure resilience to find an answer to a problem, which is mostly deemed almost unsolvable.
That’s where we spent most of our time trying to solve extremely difficult problems and questions. I think that is, has been really tremendously helpful when we transferred into starting a new technology company where again, you’re trying to do something disruptive, innovative, where there isn’t yet a known answer. So the leaf isn’t as great as everyone really thinks.
I think honestly, we, without those skills of science and the scientific method, and the resilience that we’ve got from being in the lab at 4am, in the morning, trying to crack something. Without those skills, I’m sure Obrizum would not be what it is today.
Matthew Todd
Yeah, fascinating, I think, yeah, I can certainly get a feel for how those skills, that approach, how that resilience would certainly be transferable to, as you describe it a disruptive tech platform. You mentioned a commitment almost to solving a problem that was almost impossible to crack. So, what was the awareness of the problem, then that led to the shift towards the software platform?
Beza Agley
Essentially, what was most frustrating was, we saw some really exciting stuff happen. So, we were we were in the lab at the cusp of the kind of genetic engineering revolution, when something called CRISPR-Cas9 was discovered. This was revolutionary technology that allowed scientists technologists to edit the genome in a way that we had was just so much more convenient in and then than the way scientists had to do it before. I was completely blown away by what were the obvious applications for the rest of the world in terms of personalized medicine, biotechnology, medical technologies, you name it. This is gonna affect everything that we that we know.
I was going around like this is gonna change the world. Others scientists, we’re like, well, we’ve always been doing gene editing, and we’ve been telling everyone is really cool, but no one’s really listening at this point in time. So I’m gonna go back, we’re gonna keep doing our research and publishing our papers. I was like, no, this is not good enough. I mean, we need to make things happen faster.
So if I boil that down, we saw amazing technology, amazing discoveries and innovation, that was going to take another 10 to 15 years to really impact society in a way that was going to change the world. That wasn’t a technology issue. It was a knowledge know-how training education issue. We needed to be able to put the right information in the hands of people that could do something really important with it. That was how the company was founded.
Because we actually then went round and manually curating information that was important or valuable, and taking it to the people that would do something with it. In doing that, we prove that there was indeed a market and a need and a demand for knowledge, know-how training, understanding for innovative, exciting new things. We just needed to then make that scalable, because getting on planes and physically going, transporting people around is a very expensive, time-consuming, costly thing to do. So, it was very natural for us to start thinking about how we might use things like machine learning and AI to do things like that.
Matthew Todd
What was it about that approach that you kind of manually developed, if you like, before making it scalable? What was it about that approach that was unique to other approaches that have been tried in the past? Presumably, people have made attempts at this before, but what was it you were able to bring to the table?
Beza Agley
The manual testing of the market. This is also a good idea for other for other people in technology or thinking about developing a deep tech product. Prior to actually getting in the coding, you can do quite a lot of market analysis and testing by doing things in kind of proof-of-concept proof of principle way.
If you take the three pillars of Obrizum today, automation, adaptability, and analytics, and then we rewind the clock, what were we trying to automate, we were trying to automate the curation and an understanding of the base content, that holds the value that you’re after. So we were going around and actually manually reading papers, we were talking to individuals, we were setting up events, and we were curating a base of really, really good information. We were using our human brains to do all of that great curation. Of course, the result was, was good. We had a really great curate curated base of knowledge and knowledge base, if you will, that we could then that we could then work with. So that’s our first day that we were then needed to then automate that curation piece.
Then the value was also driven in let’s say, we were speaking to someone at a drug company, they were interested in specific pieces of information, specific parts of a technology. So we could then personalize what we gave them out of this already curated base, we could personalize the flow of information to them. They gave us thumbs up and said, this is amazingly valuable, interesting, useful. So that was a big tick in the box. If we can only automate that bit. We’ve got something special.
The very last piece is the the analytics part of what we do, which is how can we measure what we’ve done is meaningful and has been successful, and give people a sense that they’ve achieved an understanding. That’s when we started to do a testing of, of what had gone in from our experience of flowing personalized information to individuals. That ended up being also being quite successful for all parties.
Matthew Todd
I see. In developing that strategy, presumably, you’re leveraging your networks to do this, but were you trying to push this out through your network? Or were you being approached to help facilitate that flow of information?
Beza Agley
At the start, as you can imagine, very much you’re using your network, you’re going out there, you’re you’re trying to connect with individuals, you’re forming networks, as well as using what you already have available to you. There was, you know, really very much us going out to the world.
Then after you start to prove yourself out there, and you start to get a good reputation for being able to do this stuff in quite challenging areas or conditions, then of course, it starts to transform and you start to get more people coming to you. That happened relatively quickly, like a lot of good ideas, they did happen relatively quickly in terms of validating that we had something really valuable. Then word of mouth spreads, and you start getting people to come to you and you can grow from there.
Matthew Todd
Yeah, and we are you monetizing at this early stage?
Beza Agley
Monetizing really early. It was something that we needed to do. The proof was, will someone actually pay for this stuff. One of the good things that we did also in the early days, we didn’t take any money out of the business. It was 100% reinvested into the company, all of it. We actually, we made a decent size of revenue. I think we made over a million before we raised a million. A good tick in the box that we had achieved some degree of interesting early product market fit in terms of a concept.
The seven years of r&d that followed was how can we automate, which is a human skill, something that we’ve been able to do with our human brains? How can we automate that and, and for the, for the good of others, so that learning information transfer can be, you know, dramatically more efficient and measurable and exciting. And then, you know, how can we do that? All areas, not just life sciences and healthcare markets? That’s what we’ve been spending every waking hour thinking about since then.
Matthew Todd
I agree with what you were saying about that product, market fit and more manual ways of validating it before you get deep into the code. I always like to talk about problem market fit is being an important factor.
Beza Agley
I think that gave us the conviction as well, that yes, it was valuable, right. If you have conviction, in the underlying value, you can dig a bit deeper and take bigger swings, bigger risks. Safe in the knowledge that there is something there. And you’re not, you’re not just throwing stuff at a wall and crossing your fingers.
Matthew Todd
In terms of that initial service-based delivery, how far did you get in? Or how many customers or what was revenue? How far did you get before you were actually able to start saying, okay, let’s now invest into tech.
Beza Agley
So interestingly, we started thinking about the technology, pretty much about a year in. Really, really, having started to develop a company, which we, once we were seen, as we knew that the idea had legs, we started thinking about technology, but we are scientists, we are technologists, this was very natural to us. But we so we really started using the kind of manual proof of concept to help fund the technology.
As I said, to where we got to, we were kind of really generating that our first serious chunk of money by a million before we then went out to raise to raise funds. Which was good, because it was it helped us to have something really viable and meaningful. Before then we went to ask investors to also see what we could see. We should always asked him to see. Then, we made the transfer across into really being a technology focused business. It was always part of the plan. Because the scalability was there.
What was really difficult was actually turning off, or at least slowing down a really successful business. So there was no reason to stop doing that in the way that we were in other than focus and attention. That was quite a hard thing to do, actually, to then decide to turn to turn off a successful service based business in light of then pursuing a technology first business all the way through for the long haul.
Matthew Todd
So the investment that was really the thing that enabled you to resource with what must have been a pretty complex, innovative, tech build.
Beza Agley
We started building with our own money, which was also good because we had our kind of prototypes. We had our MVP. We had our first enterprise technology customer. So we’ve always sold to enterprise. From the very outset. We are not a traditional edtech company. We focused on corporate enterprise from the outset.
That was a good education for us because we became more specialized in discovering where those issues were. I think at that time that we were acquiring those first enterprise customers, that’s when we raised our seed round.
Matthew Todd
I can see why you’d want to focus on enterprise customers, given the way you’ve described the platform. Presumably they’ll have the volume of data that actually makes that whole process and therefore platform worthwhile and more valuable compared to someone with a fairly sparse set of educational material or whatever.
Beza Agley
There is that they have the volume, they have all of these different departments with all of these different processes, practices, procedures, content that’s completely underleveraged. Or they don’t know what they’ve got at their fingertips or what informational resources contained therein. So there are a lot of gaps that need plugging in enterprise that we could come in and help resolve in a way that is not there in education.
If you compare corporates to the traditional education system, where things are much more organized around delivering education, for the purpose of delivering education, they’re two very different beasts, two very different beasts. We were excited by the opportunity to help these organizations which were powering economies and ultimately employing the people that are coming out of education.
So there was a lot of reasons why we were excited about the corporate space. We were even more excited when we found that nobody else had had the kind of real guts to get in the trenches and, and figure out how to really service that industry from a learning perspective in a way that we, you know, wanted to see. We want to see fantastic deep quality technical solutions for corporates that were able to solve these kind of knowledge, training skill challenges at scale, and not be lying on the ground. Actually give hard data and insights that you can make really big company decisions on. That’s what really drove us. When we entered the sector, there definitely wasn’t that. So nothing was being measured at all.
Matthew Todd
When it came to those enterprise customers, and especially the early ones, if there wasn’t really anyone tackling that problem for that type of customer that you saw the market, how challenging was that enterprise sale?
Beza Agley
When we entered in what was there already was kind of the standard linear based elearning, which is basically cut and paste, take stuff that was offline, put it online, and then people can just watch it or look at it. That’s really where we were when we entered it. There are still areas of the market that are doing that. So what we were really selling was capability, new capabilities, to these organizations.
We were coming in and saying, Look, we can organize all of this information, we can tell you what’s in it, we can label it without you having to do any of that work manually, we can do all of that automatically. You can curate this base of knowledge. Then you would like personalized learning, and we will be up there are all of the programs you work with us will be automatically ready for personalized learning. You can deliver that at scale.
You’re gonna get all of these measurements, analytics, and importantly, insights to allow you to make decisions about whatever you’re doing in that organization. We were then connecting those analytics with things that the enterprise cared about. There’s lots of different insights that pop out depending on the kind of industry that we’re talking to. So it was really a sale of capability and innovation. Of course, selling innovation at a time when, seven or eight years ago, people were not very friendly towards the idea of AI in organizations, AI in learning, AI in education. It was a really challenging sale from that perspective. But I think we were building for the future. We knew that this stuff was coming whether the organization’s themselves or the people that we were selling to knew it, we were very confident, we had high conviction that this was coming. So it allowed us to be persistent, and sell on capability and then prove that capability. It was all about proving that you could do what you said you could do piece by piece until they trust you. Then building from there.
We spent a lot of time you know, showing that we were serious business and that were going to turn up. We were going to do a great job for these organizations. Because startups often get a bad rap for being fast moving, and really just wanting to get their stuff across the line. We wanted to show that we were a very serious, enterprise focused company with serious people, that we’re gonna do a really good job and put our full weight behind it.
Matthew Todd
Was it the kind of prior service type of delivery that then gave you that credibility?
Beza Agley
Some clients crossed across with us from that service business and still with us today, actually. That works quite well. You know what else it was, it was learning the processes that you need to serve enterprise that we got from doing that.
When we turned up, we knew how to talk about procurement, how to talk about the legals and contracting, how to deliver proposals in a way that would go down well with enterprise clients.
Then of course, how to run the early customer success type of relationship with those enterprise clients, or from having learned it on the ground. The hard way.
Matthew Todd
We mentioned the analytics, the insights, I want to get into that in a minute. But when it comes to kind of your primary customer or persona within an enterprise organization, is there kind of a main persona that you typically work with? Maybe they’re in charge of education programs, training programs that you’ve worked with? Or do you actually kind of have to work your way in different areas?
Beza Agley
Interestingly, if you look at the capabilities that we deliver, those are interesting to everyone in an organization. We’re very proud of that. We don’t really want to even be siloed in in one particular corner of an enterprise, because what we’re talking about is operational efficiencies that are going to put multi-millions back in the pocket of the organization, and they’re going to help the company make more money, and be more efficient.
So that speaks to CFOs. That speaks to operating officers, that speaks to heads of HR that speaks to the learning team that speaks to everyone really. We really only need to come in with someone who realizes that those capabilities are real, and needs them. We come in at the point of need, do you have problems with onboarding times? Do you have issues with staff retention? Do you have issues with knowledge gaps? Do you have compliance areas that you need. All sorts of different requirements, knowledge, skill, and capability needs, that we can service. So we look for the problems ready for the problems, we’ve got the solution, and we connect the two. It does give us a really exciting, wide base of potential clientele, in an enterprise to sell into, which is also fun. The big jump for us was our technology initially, we started out as I mentioned, in the life sciences and kind of healthcare markets.
Then in doing that deep r&d in the way that we did over a period of many years, we figured out a way of being able to do what we do for any area. So then we became sector agnostic, so we can sell to all sorts of different corporates as well.
We’ve got a really juicy, exciting market that keeps things fresh for us. Where we could then go in and do that, can we deliver value in this industry? Yes. Can we deliver value in that industry? Okay, how far can we go? Can we deliver that in that really far-reaching industry over there? Yes, great. That’s really good. That means that we’re validating that the technology works in a sector-agnostic way. Then other questions come up for us as a business about how much we choose to focus on a particular industry.
Matthew Todd
Is that hard for them to choose how to focus and divide time if you start to open up that kind of potential?
Beza Agley
I think you go where the value is greatest, and you have to have that ruthless prioritization. We are striving to be data-driven too. We look at the data, and we test things. Then we look at where the value is greatest and we and we follow that. We don’t do so religiously, in a way that actually makes us less innovative. You got to have a bit of variance in there to figure out if there is there something you’ve missed.
Beza Agley
Strategy will always change, depending on the phase of the business.
Sometimes things when they go well, they can go so well, so quickly. So you always want to be a bit open to what’s possible out there, but at the same time, follow the data and at least put some of the biggest bets on the areas which are most likely to deliver value based on what you’ve seen from real data and real clients. We’ve done that we’ve done that iteratively and we’re still doing that. I hope, hope we continue doing that. Strategy will always change, depending on the phase of the business and all the things that it’s facing at the time.
Matthew Todd
In terms of that kind of phase of the business, but also the environment we’re all in and you’re working in. There’s been a lot of changes disruptions over the last two, or three years, so, I’ll be kind of curious as to what your findings have been over the last few years in terms of changes in behavior shifts in patterns and how that impacted you.
Beza Agley
The changing nature of public perception around AI was was really interesting to see, again, we committed, we try to try to look to the future and invest our time on where we think things are going to land. So make a few predictions that we feel we can build towards. Of course, the pandemic in terms of people really understanding the need to be able to deliver that learning training or work in a fully remote format. You need people to understand what they’re doing. Otherwise, they’re not going to be able to deliver the roles and responsibilities that organizations need. They’re certainly not going to be safe and compliant in doing so and confident in delivering and discharging the duties. So that was helping us to, to really do what we were doing anyway, supercharge the understanding the client understanding of technology and an accelerated digital transformation really dramatically. We also predicted a bit of a blend in the resurgence of blended training.
So after everybody gets a bit tired of being fully remote, they’re going to want to come back with a vengeance back into the office back into doing things. We also facilitate those experiences with digital data. So what I mean by that is organizations can do a lot of their pre-learning wherever they are in the world, and all of that data is coming back to them and to the organization and now the organizations know exactly what to focus there in person sessions on are their practical training on based on the data.
Instead of having to do a five-day corporate leadership conference where you bring everyone across the world, now you can do a one-day session. It can be more social, they can be targeting the areas that everybody needs from the data, and then the organization saves a lot of money, and everybody saves time. The impact of that training session is greater.
Obrizum is able to impact physical in-person blended or virtual training through the data that we deliver in the normal way through the cloud. So that’s been quite an exciting thing that we, that we predicted would happen, and we and we’ve been able to facilitate really nicely with our data.
Matthew Todd
I can tell from everything you said you are very much focused on what value you can deliver now to customers. But also, I would say it seems like you’re equally focused on what the future would look like, and then placing some pretty decent bets on ensuring you service that vision of the future.
Beza Agley
If you look at Obrizum, it is the only company in the world, the only corporate learning company in the world building, and delivering and showing knowledge spaces in the way that we do. So that has been great to see the reception of that by corporates and then the value that comes with that. When you place a bet, and it works, and you can innovate and bring something of value that just gives you that conviction to keep doing it. You do have to soften the edges, right?
One of the things that we learned along our journey is yes, you’ve got something that is immensely disruptive and super cool. But in order to get that embedded within an industry you have to have what we call to soften the edges. What parts of the innovation can you relax a bit? Just enough so that it’s not a shock to the system for the organization’s. It’s a bit like you know, carrying it into the business in a way that literally allows it to settle there and be and be used. That was a really good learning experience for us that I encourage others to think about as well.
Matthew Todd
Yeah, so be disruptive be innovative, but also, you’ve got to meet people where they are, to a degree in order to bring them up to that level.
Beza Agley
A direct example of that in Obrizum is we’ve got a bit of a continuum. So, an organization can start from exactly where they already are. You can deliver linear learning, if you absolutely want to, or have to, for any reason you can have mandatory nodes, you can have fixed things that only certain people see at a certain time. All of those kinds of enterprise standard restrictive controls that some organizations may want initially. Then that continuum goes all the way along to fully nonlinear adaptive learning, which is the most kind of atomic precision or predictive learning experiences that you can imagine.
An organization can choose to go anywhere they like along that journey. They can start at one side, and they can mix and match. That just makes it so much easier when you say to them, you don’t lose anything, this is only upside for you. So there’s nothing to be lost and everything to gain. Which is quite a strong position to be in when you’re selling.
Matthew Todd
When it comes to proving that value, you mentioned analysis and insights. How do you actually go about doing that? What will some of those insights look like for organizations to demonstrate that?
Beza Agley
We got our three pillars, as you know, but analytics feeds through everything that we that we talk about. So when we entered into, again, entered into the corporate learning space, there was no such thing as content analytics, really. It was a learning designer, or a team of people within the organization decided on what’s important, from their own personal experience or opinions even. Then that’s delivered to 30,000 people, 50,000 people.
So we’re like, let’s do some analysis of all of these content methods. So our analytics, start mapping the organization’s content, knowledge assets. We do that incredibly well. It’s arguably a whole product in itself, inside of Obrizum that does that work. Then we’ve now got the we’ve got the full understanding of what’s there. Then we start to collect data on individuals as they’re learning and achieving in the platform. What are their needs? What are their interests? How confident are they about things. We do it in a way that doesn’t allow people to guess game or cheat their way through the system in a way that traditional platforms do.
There’s none of this click through cheating, you cannot pass on Obrizum by clicking through and guessing, it’s impossible. That makes for some great quality data. Great quality data then means when you combine that with the organization’s needs, the content assets, and all of the other things, then you can start to make that outcome, great quality analytics and from the analytics and context you get really great quality insights. That’s where all the value flow flies out.
We focus our energies on connecting those dots and giving it back in a way that people can take immediate action. Whether that action is restructuring a team or that’s up to the organization to make the final decision, where really that decision making support. So we want to provide great quality data, and really good quality support for decisions that they were already going to make. How about you make them now on the basis of least having good quality, meaningful data?
Matthew Todd
Does that mean that in terms of your actual products there are different stakeholders then that derive different types of value from the platform. So you’re kind of catering for multiple different kinds of needs?
Beza Agley
Well, there are you’re absolutely right. There’s value that’s very different depending on who the stakeholder is. So if you’re someone who’s coming along to get a new professional qualification in an area, and you’re really time crunched, you’re already a manager working full time, you’ve got family at home, you’re extremely busy. Then your time is a premium. You want to be able to get that new piece of information, that new qualification that is going to help your career, take some of the financial pressure off when you get a bit of a promotion.
Off the back of that you want to be able to do that quickly, efficiently without compromising on the true value of that qualification because you actually need the information in there to do a good job. In that case, speed to competency as being able to accelerate that in a meaningful way for you is going to be a game changer. If you can get something done three times faster with Obrizum, than if you did things down the traditional route, without losing the actual information that you actually needed, that’s just a really brilliant thing to do for someone.
For the organization, because it’s more attractive to these individuals, you’re going to sell more. Then people are going to come along, and they’re going to want to buy your certification product more readily. That’s going to be great for revenue and upside. Also, you’re going to really understand your customer base, what do they need? Where are the gaps? Where are the opportunities?
So when it comes to you producing your next piece of training content, your new certification, as well as advising those people on what they might want to do next, you’ve got all that data at your disposal. Again, good quality data to make those decisions. We can serve as multiple stakeholders with the data and analytics, but in a way that’s genuinely beneficial to all parties, which is the really exciting bit that we feel quite passionate and pleased about that these kinds of analytics are benefiting everyone in quite a virtuous way, which is nice. Everyone stands to benefit from the platform, the way it works that you’re describing, there’s no one that loses out here. Everyone really is better off going through this process and using this technology.
I think for us as a business, culturally, that’s something we believe in very strongly that we want to be doing something that is actually useful, meaningful. When we wake up and we put all this energy and time into inventing things, we want it to be moving the dial in, in a good way, it’s not just about making money, there’s many ways to make a quick buck if you like. But when you when you can make money, and actually give something of value to all stakeholders, then it’s something really that you want to just go all in on and really push.
Matthew Todd
I can see the enthusiasm for the problem as much as the enthusiasm for the solution and the enthusiasm for the business, which is really, really great to see. It is critical to any meaningful and lasting success, to be honest.
Beza Agley
The fun bit of what we do, I mean, now we’re very, very much a business. But the technology, the addiction to problem solving in our tech is really cool, because it’s really hard to solve some of the problems that we solve. We’ve got that scientific addiction to solving those sorts of problems. It’s nice to have something that was really challenging, and to be able to invent solutions with the great people that we’ve got on our team.
We’ve got some of the best machine learning AI talent out there, but also the best media, creative talent, business talent. We’re really a big mix that solves problems together, right into the customer success teams that are working with that technology and our clients.
Matthew Todd
With that kind of one eye on the future, and what that technology can enable, and obviously lots of talk online at the moment about AI and GPT and everything else. What do you see as the next kind of iteration of the future in your space in that learning and knowledge world?
Beza Agley
This is a very exciting, busy time with AI. We have our own views on things, when it comes to responsible use of AI but also you want to be able to be on occasion, it’s quite important to be able to understand cause and effect in what you’re doing. So having something that systems can be explainable, is very important.
On many occasions, if you’re dealing with very serious information, especially when there’s any risk to life or anything that is to do with serious things that you need to understand. We have our own way, which is proved again, to be correct in that prediction that we can always whine back and figure out why did why did Matthew go down that particular road on that particular day for that particular subject? That’s was really important. All credit to our head of AI and innovation who was doing this long before. The kinds of regulations that you see are in place today. That’s an area that we’re keeping our eye on before getting too wrapped up in in areas that you know, where other people have full control over how models have been trained and you don’t really know what’s what’s popping out.
You got to have got to exercise caution, but at the same time, find the values, find the bits that it’s safe to use in the way that you might see fit. We feel like our commitment to doing things the right way around will ultimately benefit us. I think it’s gonna be a very exciting time, there’s a lots happening, the world’s waking up to the idea of using technology for these things.
The great thing is that our market is every company in the world that needs to do learning and training, and we’ve got such a head start on everyone, that even if there were another kind of 50, competitors, it still wouldn’t do much to stop us at this point.
Matthew Todd
I know, a few companies in the EdTech space that are doing pretty well for themselves, are growing. But to be honest, I see a lot of very outdated tech wise products that are still very linear, or maybe they’re overlaying very simple interactions on top of media, but it still feels very dated in terms of technology.
Beza Agley
It was shocking, but our eyes lit up when we saw that actually, because it was like, wow, here we go. We lept quite far into the future, initially. What’s good is that it took us some time anyway, to actually make that a viable scalable enterprise product. The market caught up with us if you like, and, and now we’ve got something which can really bring that meaningful value.
It was a an area that was underserviced with the highest end of tech. It’s strange, because you think that the corporates who are doing a lot of the stuff that we use, and we build, the technology that we play with every day, would be able to take care of that. But of course, nobody can get to everything, nobody can be five stars, across every single pillar. So, there was a gap, there was a genuine gap, there was an opportunity, and we ran in there.
It is a high barrier to entry market, by the way. It is a high barrier to entry thing to serve enterprise, the sales cycles can be long initially, the requirements in terms of data security are also high, it’s an expensive market to tackle, which is why we felt that actually a lot of competitors hadn’t gone there in the way that we wanted to or early-stage businesses couldn’t because they couldn’t afford to.
Again, we benefited from that resilience that we could survive on not very much. We were also making money we could reinvest, so we could survive the time it would take to become an enterprise company.
Matthew Todd
For anyone, considering what market they should be serving, should it be enterprise or not, I see a lot of people trying to start small and then move up. But then the problem is the problems they’re solving aren’t the problems the enterprise customers have gotten? By having that service level up front, you prove that you know the problems of that customer base at that level.
Beza Agley
It’s a whole world. It really does take time, you need to take the time to learn and understand what you’re dealing with, and the kind of pitfalls. It’s not always clear until you go through speaking to 30 different enterprises. The patterns start to emerge. You don’t get it from one or two, because they’re so complex.
Matthew Todd
Yeah, I can imagine it is a tricky route to go down, but definitely a valuable one where you were able to see that opportunity. Thank you for sharing some of the lessons along that way and details about the journey as well as giving us some really good insight into innovative disruptive technology.
The overarching theme, I think, for a conversation has really been that desire to solve hard problems, and just commit to actually solving the problem because the problem is worth solving. Just before we do, kind of wrap things up, are there any other kind of pieces of advice you’d like to leave our audience with today?
Beza Agley
Commit to doing what you feel is right and that you are the areas where you’ve got high conviction and, and if you’ve been able to prove it, and you really do feel that you’re going in the right direction, don’t be distracted by others.
Beza Agley
Stick to your guns, but also be guided by the incoming data. Be sensible, be pragmatic, soften the edges where you need to, and don’t run out of money.
You’ll get lots of advice from lots of people. But have those core areas that you believe in when you’re developing something, and stick to your guns, and, but also be guided by the incoming data. Be sensible, be pragmatic, soften the edges where you need to, and don’t run out of money.
Matthew Todd
Thank you very much for taking the time today.
Beza Agley
Thanks so much, Matthew. Really appreciate it.
Matthew Todd
Thank you for joining me on this episode of Inside the ScaleUp. Remember for the show notes and in depth resources from today’s guest, you can find these on the website insidethescaleup.com. You can also leave feedback on today’s episode, as well as suggest guests and companies you’d like to hear from. Thank you for listening.