Jan (00:00):
So much stuff is happening. You cannot just jump on everything. If your product is not well set up for it, you're going to build a solution that's not really helpful for anybody. It's not going to be successful, and if you're not going to be successful by you to even invest time and resources in that.
VO (00:14):
Welcome to Spotlight on a podcast about how companies are built from the people doing the building one messy, exhilarating decision at a time.
Ben (00:22):
Welcome to a special edition of Spotlight on I'm your host Ben Fletcher, and I'm really excited to be here with CEO and founder of N eight N Jan Ober. Hausa. We're really excited Jan, but maybe for those that don't know, tell us about what is NAN.
Jan (00:36):
Sure. First, thanks for having me, and it is an AI powered workflow automation platform. It really allows people to connect everything with anything. It actually goes much further than that. You can really build AI agency can build AI powered applications and it really allows people from developers to geotechnical people to do things that was only possible before for developers.
Ben (01:02):
Today we're announcing that Excel is leading N eight N series C and we're really excited to be working together. You all are powering a lot of agents, you all are the brain behind orchestration for a lot of the AI workflows that are happening for creators, for developers within the enterprise. A lot is happening and a lot of that is happening on N eight N, but a lot of folks don't know about NAN and this is the first time they're hearing about what you all are doing and becoming one of the gems and one of the leaders as an AI company. Maybe walk us back and start from the beginning around your remarkable story about how things got started and in,
Jan (01:38):
I actually have probably a very uncommon background. I didn't study computer science. I actually studied auto. Visual media means I come more from the kind of movie side of things. I did effects from movies originally. I worked on the actually effects and the kind of later on I became a pipeline entity. That was my job to make the life of the artist more efficient, easier, which obviously involved a lot of automation. One thing I saw there is those people that are very smart, very well paid and quite technical, but they were always reliant on me or other people like myself to actually do the things they wanted to be done. They could have had so much more impact if they would've been empowered and they weren't and is doing exactly that For organizations today,
Ben (02:21):
The best founders and the best companies that we get to work with, they always start with a need. They see that or they wanted to use the tool and that clearly happened with you and where you were at in your previous industries. So when you decided that you wanted to build this tool, did you leave your job? Did you go all in? Was it a gradual, how did you build this nights and weekends, get the community going to get things started
Jan (02:42):
Between having the idea and actually solving the real issue and actually multiple years went by a company I was working for that was called Digital Domain. They already had actually a solution there that kind of did something very similarly, but it was like XML based, literally no non-technical person would've ever used it,
(02:58):
But it gave me the understanding that something is actually possible. Then actually years later I then worked on a different startup myself and there I realized that even though I was obviously technical myself and I could program everything I needed, I realized that actually spent probably 90% of my time re-implementing things that have been implemented before. The most basic example is something like Get us down GitHub, send a message to Slack. Each of those pieces have been literally been implemented millions of times before by almost every developer out there and this is never the most fun thing to actually do. And then example, I was wondering why do I spend most of my time on reimplementing things that have been done and they're not very joyful to do? Why don't I put the most of my time and resources in the kind of things that actually are special to my specific need and that is when I thought about there should be kind of legal kind of building block experience to make sure I have this building blocks for the reusable components again, get information from GitHub, get something from Slack, all the things that more or less most people need and then allows me to spend my time and resources on the things that are special for my need.
(04:09):
2018 is when first again had a data actually implement it and solve it and then between actually writing the first line of code and actually deploying NN was like another one and a half years while working at another startup, working at a second startup to actually earn some money, but because I already had wife and one child back then so I would had needs some food and some shelters as
Ben (04:33):
Well. Of course, of course.
Jan (04:34):
And then after again roughly one and a half years in June, 2019, the product felt finally ready to at least share with some people and after I did the soft launch, I saw the first people reaching out. They really got some value out of it, they received some feedback and then really did a proper launch in October and then it really took off. People just loved the product and they saw even more value. They started to contribute to the product, to the documentation. They joined the community and then really the growth started
Ben (05:06):
When you were building the platform. Early days you've done a really good job of building a very technical platform for developers and also non-developers, but you've made sure that it's simple but you can also get the most out of the platform. How do you kind of balance the two?
Jan (05:20):
It's actually not easy. I think for me the most important thing was always kind of building a very flexible and powerful platform in the end because I think the problem with this kind of low-code tools was always that they seem amazing in the beginning. You get a lot of value out of them, but actually when you want to bring it in production, you're there to 80% and you just think I need one or two more things. Then you're suddenly stuck and then you kind actually bring in the time or the time you saved your payback five times to actually get it to production use case. But I think very often those kind of tools have a very bad reputation and they wanted to avoid that, but obviously you cannot avoid that problem. You have to give something there and what we gave in the end is what people talk about very often is we have a steep learning curve.
(06:05):
I think you can only make the product, you can only go down to a certain level how simple you can make it because if you make it even simpler again at some point you have to give something up for it, which is again flexibility and power and again, it was always at the center of and keeping flexibility and power. Sometimes people have in the beginning a slightly harder time, but once they understand how much value that they're generating, they actually get so much more value out of the product and there's also bias stick around because it seems like the whole world is suddenly opening up for them. It's just like, ah, I thought I could do X and then I realized I could also do Y and set and so many other things. I cannot just use it privately. I can also use it work, I can also use it there. I think that's really the amazing thing and I think it worked out very well in the end.
Ben (06:48):
You've done a really good job of striking the simplicity for folks that are starting out for the first time, but it's also resonated within enterprises and enterprises are standardizing on the product. How did you do this early days to make sure that you had a vibrant community, the effusive love was there and you struck that balance with the community?
Jan (07:07):
Community was always very important for me. I think if you really want to achieve what we want to achieve to really become this default tool out there, you need more like an army of people that really vouch for you to bring you into the world and talk about you and actually bring the value that they're never able to get as a small company at all means. That's why we focus very early on building up this community out there. If you start an open source company, you can necessarily say, Hey, you want to have a great community, actually have to invest in it as well. That's why from the very beginning I was there answering questions, I knew the product wasn't perfect and you have to make up for it somehow. It means if people run into this obvious issues in the beginning, I have to be there and help them
(07:53):
And again at some point you couldn't scale in mode and you have to have other people that kind of do the same thing and it's like we were very good in if somebody had an issue that came with the problem to us and we were like within minutes, the amazing thing we also see now is now we don't have to do it by ourselves anymore because of this amazing community we built. They help each other. That kind of makes the whole thing scalable again. I think that's the amazing thing about the community. I think you start in the beginning to give, you give away this kind of product for free, you give away the support for free and at some point you get so much more back and that's why it's so great to actually build a product that really has this amazing community behind it.
Ben (08:29):
Over the last couple months you all have seen explosive growth, so on the community side now to over 600,000 developers that are on the platform and active over a million instances that folks are running automations on and that's grown over 10 x this year, how do you now manage it at scale to make sure that the community continues to feel that personalized, that intimate nature that they continue to feel?
Jan (08:53):
Yeah, it is definitely getting more and more challenging. One thing we definitely try again making sure we use the community executive for that for example, we built out programs to make sure the community actively helps each other out, so again, as you just pointed out, we have a lot of users out there and we still have a rather small team. As of today, we are probably around one 40 people I think,
(09:17):
And then obviously if you have to service that amount of people it simply doesn't work. You cannot do everything by yourself, so we built out a lot of programs for our community where for example, we have navigators in our community to actually are there to help each other community members. We have a program where if you answer a question of another community member where you get kind of points and you have a leaderboard and the top contributors that help other people out to get also prices and like this, we are able to still provide a good experience for the most users and at the same time still scale. The nice thing is again, if you show people that you actually care about them and you actually want to empower them and actually you really value how they're helping out other people, then it's really something those people also get value out of it and it's starts this kind of flywheel as well where they grow also not just a community but also people that actually want to help you and want to make other people more successful.
Ben (10:11):
Amazing. So you had a vibrant community, you had a workflow builder, you had all these integrations and you had the foundation for building this platform to bring automations wherever folks were at, whether it was personal or whether they were hobbyists or also into enterprise and then AI came along and you famously said that it was either going to be the demise of the company or it was going to be an incredible opportunity. One, how did you stay on top of these trends to make sure you knew what was the most advanced models and what models needed to go into N eight N and then two, how did you make sure that you weren't left behind? A lot of founders can see these things but it's hard to really go and catch that wave or be in front of it and then see that kind of growth that you all have had.
Jan (10:52):
It's actually really a hard thing because there's obviously, especially with the ai, there's a lot of stuff is happening. I think the thing is also important to realize is very often you don't have to be the first one. Sometimes we can also be just seeing where the market is moving and then kind of react to that. In this case, we took a different approach. We really tried to be the first one we started pre AI and over two years ago from two and a half years ago or I would never have thought we would ever go in AI at all, but then if this kind of opportunity is presenting itself and you kind of just see what you already built and the thing like AI is enabling and you just realize at some point that this thing that fits perfectly together, then you really kind have to really seize it and it can make use of it. So much stuff is happening, you cannot just jump on everything. You really have to a combination of what you actually believe in what's going to happen and the same time where I really think is where can our product provide the most value
(11:54):
Because you can do everything but at the same time if your product is not well set up for it, you're going to build a solution that's not really helpful for anybody. It's not going to be successful and if you're not going to be successful by you to even invest time and resources in that so it's better to lease sometimes some things behind to not do them rather than trying to do everything and then doing a very bad job, they're doing a few things versus doing everything kind of half-baked. I think it's generally a good recipe.
Ben (12:19):
Do you think it gives you all an advantage that you had the platform previously and you had built all these tools and then integrated AI later? We have the conversation internally around are the not legacy but the incumbent providers, are they more well positioned for AI or the AI native companies, which are the ones that are more poised to take off and really run and become leaders in their space?
Jan (12:44):
I'm quite happy that we started pre AI because there's quite a few years already in the product, a lot of people a thinking how do you build a ui? How do you provide the best experience? How can you onboard people? All of that kind of knowledge is there already. I was obviously told you a totally different beast. We have to figure that out as well, but I think some things are always true and I think this kind of knowledge, how do you build a great use experience, this kind of knowledge we already had and we could bring it into this AI world as well. Things that you need for AI for example, you need a lot of integration, you want to connect to other systems like all this stuff was already built out. We had already not just advantage of figured out the ux, we also had already a lot of stuff prebuilt which became quite helpful as Well's.
(13:29):
What I meant before is sometimes you just have to take the opportunity when something fits perfectly and there really it felt like it did fit perfectly. A lot of AI companies, they focus solely on ai. They think that AI is a solution for literally everything. There has to be a human in the loop for certain sessions. For some parts it definitely makes sense to have code involved because AI is simply not necessary. It's much faster and cheaper to do it with code and then if the AI piece as well is like you want to have all three pieces working together. Again the same described with the team before that makes it stronger, that makes it better. It's not just one thing as a solution like mood combining multiple things that kind of work in tandem normally I think they're much better and strongest solution. That's why I think also our user base has probably a much easier time getting actually production use cases live because we are not just relying to a hundred percent on ai, which is for some use cases not ready yet.
Ben (14:17):
So y on early days you talk about how AI was going to be the demise of the company or a huge opportunity. You had all the building blocks, you had the visual tool, you had the integrations and then you saw that this wave of AI was coming. How did you all think about it? What were the things that you integrated? Were there things that you were nervous about when you wanted to bring AI into the product?
Jan (14:39):
When it actually came up the first time like ai, I really honestly was a bit scared because what was really clear is it's changing the whole game. It's like things just months before I thought would be impossible to take a very long time just suddenly became very easy and then you obviously wonders what does it mean for your business so you really have to really take a step back and realize, okay, what makes us special? Where can we stand out and how do we fit into this kind of world? Some companies were doing amazingly and other ones obviously not that well anymore and then I looked at companies that were doing amazingly and especially also ones that suddenly started to raise very high rounds and then they intent to understand what changed there and one company I saw was actually Pine Cone. What Pine Cone was before, they were like a vector database and whatever were after they either AI database so it was very clear that they become part of the value chain and again if I wanted to make sure that N becomes long-term sustainable and we can have a part in the future and especially than just being around actually being an important player, we had to make sure that we also become part of the value chain as well.
Ben (15:51):
When you talk about this moment and how it was scary and there was a lot of things that you all wanted to do and seeing that things were changing, it's really hard to change where you're at. You have customers, you have revenue, you've built a lot of product and I think there's a lot of founders that have done similar things, but what were some of the things or how were you able to then take the company and reposition, put in the things that you needed to the right tools so that you were one of the most important building blocks for AI going forward
Jan (16:23):
We realized we have to move fast. What we actually did is it probably was one of the interesting most short of times for quite a while, again because I literally could go back to full-time coding again for quite a few weeks. It was like in the end it was myself and one and a half other develop developers. We kind of really started on that project, say, Hey, we think that's super important and they kind of put our resources behind that. The think between the decision to actually build it and releasing it. I think probably six weeks went by and then we released the first version, which wasn't perfect at all, but I think very often it doesn't have to be perfect. You of get something out there and then kind built on top of that and I think that was and
Ben (17:03):
The first version was workflow tool with an LLM that was embedded for doing different triggering or starting the workflows that you have.
Jan (17:11):
In the end, the first version went further. We already had where we, early on we had an open I node where you could kind of use the rest API and I think that's what more or less most of our competition had as well. What we really did there is allowed people to build proper agents within it and it's like you're not just calling it an rest API like you have an agent, you change all the problems, you can add your different L lms, you can have a vector store, you can have a memory, you can have multiple agents and then connect it with all of the tools we already had out there as well. I think that is again what was very different back then and on this is still a thing that most of them still weren't able to integrate as well as we were able to.
Ben (17:52):
Some of our best founders have kind of paused, seen the opportunity and gone for it. I think about Alex Wang at scale and what he did with positioning the company around reinforcement learning around what you all have done around N eight N and building nodes with open AI into your product. There's a lot of things that need to happen in order to get there. Are there challenges that are associated with that? Either positioning the company, transitioning where you're going and just trade-offs that you all are making to really lean into where things are going in ai?
Jan (18:27):
Actually quite a few first every time you take a totally different direction. I think first internally people are wondering, it's like what does it really mean?
(18:36):
Some people were quite excited internally, some people thought it was a totally wrong direction and obviously you have to try to take them with you and obviously very often it takes some time until a realized opportunity. At the same time, I think especially with ai, it feels like it's a very different rhythm. In the past we could very easily plan out 1, 2, 3 years where you want to go with the roadmap. It's not possible anymore because literally every week something else gets released. I think it's only important to have some kind of roadmap where you want to go, but I think you have to be probably more willing to kind of change it.
Ben (19:08):
Amazing, amazing. You all have done a great job of staying on top of everything that's going on within AI and making sure that N eight N incorporates all the latest learnings. I know you and David, the VP of product sit down once a week and talk about everything that's going on in AI and what needs to be incorporated into the product. What are other things that you all do to stay on top of AI and everything that's changing?
Jan (19:32):
There's so much content out there these days. It's like you really have to kind of try to follow the right ones and getting the kind of right ideas out there, but in the end it is really just again talking it through in the end very often it's like things everybody heard. We have a list of stuff we want to talk about and then we just talk about the different topics and then again, very often we talk through some stuff, kind of get a rough understanding is how they could actually work, how they could fit into end end and then sometimes they all directed to the product and sometimes we just pause them because we just realize, hey, even though we realize it's important, it's not the most important thing right now and then we very often marinate over time. It's like, ah, actually now this thing emergently can work on top of what you already created there.
Ben (20:19):
You've always been extremely fair, very transparent with your employees. They love working at N eight N, they feel they can go there, do their best work and they can continue to be challenged. Have you seen things change pre AI and now post AI after things have exploded?
Jan (20:34):
The biggest change you probably saw is we talked about before we have grown the user base 10 x and the company group little bit probably two XA little bit more in the same amount of time. It definitely got very busy. They
(20:47):
Think we always were very proud that we realize it like it's a marathon, it it's not just a sprint. I think that's why we try to find a quite good work life balance. I think right now it's definitely, especially in some departments in the case anymore right now, that's why we are hiring very fast and also try to even automate more and use more AI just to get it to this place where we think it's actually best for the people and in the long term. For example, we also internally set the goal to become the first 1 billion Euro R company with less than 500 employees. We want be a big company in impact with quite few employees. I think that is where we do a lot of thinking right now, how can we build this kind of organizations? Where do we at headcount, where don't you add headcount?
(21:34):
Headcount, how does it work for departments like engineering and sales? How do you build this kind of organizations, this organization? I think especially also important because we are an AI automation company and it's important that we figure it out because that's what we are selling to our customers. We have to be the example and only like this, we can actually help our user base to do the same thing. I think that is definitely something that's changing me. Just say we cannot operate the same way anymore like you did pre AI because the world literally changed so you have to find ways to really incorporate in the right way and again I think that's probably one of the biggest changes right now.
Ben (22:15):
Tactically how do you all do that internally? Because a lot of people talk about it and say they want to be able to be efficient as possible with ai. They're not going to hire new employees until their team has become AI native or fluent in the things that they do internally. You all are absolutely practicing what you preach and doing that with N eight N with everything that you all want to automate, but what are some of the things that you look for when you hire? What are the things that you all do within your teams to make sure that those things are happening internally?
Jan (22:47):
I say with everything I think we said could still improve a lot. I think it's just something you always have to be aware of is you could always do more. We definitely try and one thing that's actually that's always very important for me and I think the rest of the organization but also pointed out before is our employees are generally they're very happy. I think we have an employee NPS score of 95 to a hundred depending on the half year when they do it. I think there's obviously some people want to keep, people are very happy and they really enjoy the culture we have built. I think that's actually one of the most important things. Again, to actually make sure we hire the right people married with making sure that the people you hire actually love what they're doing because one thing we definitely want to avoid is kind of burnout and when do you burn out is if you have a lot of work that you actually don't enjoy.
(23:36):
If everybody in an organization is actually having fun when they actually do work, like people again, they love for they're doing, they provide so much more value, they get a lot of fulfillment out of it and everybody's winning. I think that's generally what we are always looking for. Intern and external is kind of creating this kind of win-win situations because then everybody's winning. It's like that's how it's growing is also again with the same community again, we given something, they're winning back. We are winning as well. I think that's probably part of the success we had interned externally.
Ben (24:10):
Amazing. When you all hire, what are some of the things that are really important that you look for because finding that type of personality and folks that can do their best work but are enjoyable, they're loving it, they're happy, they're having a great time. What are the things you look for in the interview process to find those folks?
Jan (24:25):
One thing interesting, we don't have it as set as a value but people really value a lot is that people are humble.
(24:32):
I think that is just makes such a big difference. If you have people that have really amazing at what they're doing but don't brag about it, that's just how they are and they're always there to help others. No matter how busy people are to help, they always find time for others as well and also people just care. That's actually one of the first employees I had to let go at end end was not because of the performance. He was doing a good job, but everybody had ended and cared very deeply about what we were doing and he didn't have the same excitement. I think that made a difference in the end, if not everybody's excited of what they're doing, then they're not the right people. I think especially it's getting a little bit harder these days. In the past, people joined and actually had to give something up.
(25:20):
They actually got a lower salary in the end and they have worked for one of big companies or went to an organization where there was more risk. Now we suddenly become this organization where people really want to work for and I don't want to have people that kind of start because they think they're going to become a millionaire because of the shares they're getting. I want to have people that actually work there for the right reasons because they're excited about what we are doing. I think that makes a big difference. That's what we are looking for.
Ben (25:44):
When you're trailblazing and creating the leader in a new company and not being humble, which you are to the core, how do you balance that with now being one of the most interesting AI companies out there? Probably one of the best businesses out there in terms of gross margin and retention for what you all provide for enterprises and what you can do with ai, it is pretty incredible, but balancing that with the humility and not wanting to share that and with what you all have in your core, how do you balance the two to make sure that you do the best for N end?
Jan (26:19):
Yes, actually, I think that's probably something I'm struggling with myself. I think there's probably a problem as European is we don't break and I think obviously brought some problems as well. We definitely had a long time visibility issue, especially in the US where people, even though we had quite some scale already, people didn't know about us and we definitely have to kind of guess that a little bit and probably talk more about the things that are going very well and be more outspoken. It's changing and people just see how much value we are providing. Maybe we are not very American that outspoken, but in the end most of us ask are European and we also getting more Americans in as well, like opening our New York office. We just opened it very recently and want to expand much more there, so maybe you're getting some more of that DNA into the company as well.
Ben (27:08):
Amazing, amazing. Well, I've always said it starts with coming from product and you all now have this incredible product and incredible community and incredible business and so it can come natural to you, but it's also very authentic, which is really nice to hear is that you have a great business, you all are enabling a lot of amazing use cases and now you all are at the forefront of what's happening AI, and so it's awesome to see. Maybe the last thing is maybe we just talk about what's the future for N eight N end. You are raising this large round, you've more than 10 XD on the revenue side and on the company side you talk about you want to connect anything to everything. You want to hit a billion dollars of revenue with less than 500 people. What are the next steps? What are the things from product, what's the vision? Where do you all want to go next?
Jan (27:57):
I think it's probably also very closely linked to ai. I think AI is still in this phase where people create amazing MVPs but not everybody gets real business really out of it.
(28:08):
We definitely, again, because of our combination of human code and ai, I think our use base is a little bit ahead there to other AI products, but really making sure that the product allows our user base to move into this kind of production stage where they really automate and use AI in business critical use cases that becomes much more important, so again, right now it's just about building. We really want to move in the direction about testing and monitoring and part of this evaluation as well. I think that is really like the COVID because what it's really about is providing really value for the users. I think that is what AI is always promising, but it's not get delivered that much and I think we really want to really help make sure that real value is actually created. We really want to empower more and more people to use AI for the fullest and it is really set up very well for that. What we really want to become is what was Excel 15 years ago for spreadsheet is, and it should be become the same for ai, so really empowering everybody to use AI to the fullest and get real value out of it is like people should build the agents for private use cases and SMBs in the largest enterprises up to government organizations, empowering everybody to get real value for the business, critical use cases through and through ai.
Ben (29:29):
Amazing, amazing. Thank you for joining us. It was an amazing conversation. Thank
Jan (29:33):
You very much for
Ben (29:33):
Having me and appreciate the time.
Jan (29:35):
Thank you.
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