The AI Product Rule That Grew n8n to $100M ARR | Jan Oberhauser, CEO n8n

I just went to your website and you have this big [music] button that shows your GitHub repo with almost 200,000 stars and it's right there next to signing up. >> Obviously we prefer people use our hosted solution and actually pay us because obviously it allows us to reinvest the money, but at the same time like if you use us for free that's totally fine as well. Like they're just two different ways of using n8n. >> Jan Oberhauser, CEO of n8n. >> What made us special is like very early on kind of really focused on the community very heavily. Created an an amazing platform but also really ensured those people had a great experience in there. >> They support each other. How is it possible for people to train their own agents as they execute some of these workflows? >> Then it's very powerful. [music] You can build very complex systems there. I can just say here something happens there that actually sent to do something with it. Output sent it to another agent then they can use it again as an input and people can build really really complex use cases. >> How you seen the companies that are more advanced kind of take that AI option metric a little further down the line? >> AI cannot do anything. It's not supposed to be doing. >> Hey, this is Carlos, CEO at Product School and your host [music] on the Product Podcast. My guest today is Jan Oberhauser, the founder of n8n, [music] the open source automation platform that's crossed 100 million in ARR at a 5.2 billion dollar valuation with nearly [music] 200,000 GitHub stars. In this episode, here's what we cover. The fair code license bet, why he rejected traditional open source and still [music] won, why sprinkling AI on top kills products and what to do instead, how n8n beats Zapier and Make by giving the code away, who should actually build automations, >> [music] >> hint not the product team, the metric he uses to know if an AI feature actually works, how one company wrote 75% of support through an n8n agent with happier customers. Let's get into it. Welcome to the Product Podcast, Jan. >> I thank you for having me. Great to be here. >> I had to start with this. Why did you call your company n8n, man? >> It caused a lot of confusion out there. And the simple reason is I'm a solo founder, so then like I started n8n started to program and then you had before I launched it I have to kind of I need a name for the product, but I also need to kind of make or break the company. So, I just said, "Okay, like I find a name. I kind of time box like an hour." And then I just started. I wrote down like all of the names I I liked. And all the ones I liked they were already taken. As you know, all the good domains are always gone. So, one name was left, which was Note mation. The idea was there's kind of it's a note based system and automation combined it. But the name was quite long. It didn't really look nice or exciting. So, I thought, "Hey, maybe I can make it more exciting." So, I thought I'd do the same like Kubernetes K8S and then eight letters N. This is the same thing actually. And then N and N stuck. N and N.io was available. After the seed round I could buy N and N.com for 5K as well. We don't use it right now, but at least we we own it to make sure it doesn't get more expensive. You know, everybody has to say that's a strange tongue breaker. And there's so many different ways of saying N and which I didn't even imagine like from like nation, naten, Nathan. We have N and N. And acht N in German. Like there's like million different ways. Quite exciting to see what people come up with. >> You made it work. Like people recognize the name. It's obviously not not very common, but still, you know, like the product works. People will find a way to remember it. You started just this journey over seven years ago, like way before LLMs became mainstream. So, I'm very curious to but you should really I start hearing about you maybe two, three years ago. So, I'm very curious to know kind of how did you see this opportunity of LLMs and you said to pivot into that. >> It's just that we started with AI and honestly I didn't imagine at all we're going to ever do anything with AI at all. And but then obviously ChatGPT came along. We saw like a large language models getting kind of strong and strong and better. And so, we just were wondering like like how do we ensure that like N and N isn't just part of the the like it's not just a future of N and N, but we actually participate um in this amazing AI wave which we kind of realized will be the future. So, we kind of took a step back and we just looked like what are we doing? What are competitors doing? And all of them are including ourselves honestly we just sprinkled some AI on top then and then we thought okay like to really kind of make use of it really make sure we are part of the value chain that actually people are not just using AI with and it and but actually people building AI powered applications agents with and it and and that's when we then realized that we actually ended and fits in perfectly like what do this kind of large language models actually need they need a lot of data from different sources like no matter if it's your from Google Drive is it from your local files is it from Salesforce or whatever it does something with the data it transforms it makes a decision and then it outputs the data again again into a system or writes it to a file again and so on and we just realized we had all of those pieces already in place but the in between piece like this agents that has had to be done like externally or like just very limited by HTTP request nodes we just realized by really kind of adding this kind of capabilities like in front and center of end-to-end we really kind of are able to kind of create something really amazing and powerful and just to not just again kind of allow people to build simply automations but really build really powerful agents as well >> Already that this should make sense in retrospect right because we're all using agents and understand now the concept of agentic workflows but back in the day that wasn't obvious at all and there was also a lot of competition there were so many other platforms that maybe were more well known at least in in the US and you guys are you you you announced you crossed over 40 million dollars in annual recurring revenue you had massive funding rounds that are latest one at 2.5 billion dollar valuation with participation from Nvidia so there's definitely something unique there so I'm curious to know if what was that kind of unique thing or secret sauce that helped you be on the map with this incredible traction >> Sure and let me first to clarify we actually over 100 million euros ARR already and we are valued at five 5.2 billion dollars right now with kind of our launching our partnership with SAP so we are growing so nice we don't announce the the exact revenue numbers but just people we are we crossed 100 million ARR a while ago. And so and and and the question was like what what enabled that? The this experience user goals. >> By the way, I love to be wrong about those things like oh my god, now it's over a hundred million dollars ARR. So, there's so many tools out there that were trying to do something similar. In fact, you were in the first mover, right? There's so many other platforms like Zapier and Make and others that where they already had had more market share. But you kind of came out of nowhere in the last two three years and achieved this incredible traction. So, I'm curious to know it. Earlier, this what helped to get that initial tailwind? >> I think like from the very beginning and it and was all about like power and flexibility. Like I think this what they kind of missed with a lot of the existing platforms. Just kind of think a lot of the existing platforms you kind of got something built very fast. But if you if you wanted to get a bit of and bring it in production or you actually kind of wanted to scale it, you kind of this this platform really maxed out. That's why we kind of made a decision kind of really focus on power and flexibility and kind of really um ensure people don't outgrow our solution. Um and I think what what made us special is like this focus but also our focus on the community. Very early on kind of really focused on the community very heavily and kind of created an an amazing platform but it also really ensured those people had a great experience in there. Like they support each other, we supported them. We provided um a great great support. We we create we had uh online events. Now we we do offline events and we just making sure like putting those builders which is really at the center of and and is about empowering people. And even our kind of one of our core company values is we are builders. Like like we are builders, our community are builders, our users are builders. I think like having like this this kind of this alignment between um everything kind of really ensures that you kind of attract the right people. Um and it really kind of provides that value and I think especially that in combination I think we grew very nicely the first years but when it really took off is when you then focused on the AI as you mentioned before. But I think there were a lot of other platforms out there but I think none of the other platforms kind of really had like this kind of very deep, very um powerful um AI capabilities. Like we had them, like all of them had like some AI integration, so you could kind of call out to I don't know, an open AI or anything like that, but like what we really did from the very beginning is kind of really ensuring that it's not just building like calling out to an to an to an API somewhere, but you actually built like proper agents, like agents anything from memory. You can have like multiple models, you can have um different tools, you can configure them very easily, you can add scripts. Um the kind of this kind of power and flexibility that was nowhere else available, and I think that's what people really craved. In combination obviously of other advantages like um for example, in case of force us, that people really appreciate that tested and know where the data actually lies, knowing they can run it behind a firewall, they can scale it um they like we have a big focus on on reliability, like reliability is scalability, and and auditability. I think where you can just very easily understand what's actually going on there even in more complex flows. >> Let's unpack some of the concepts you mentioned because you're right. I mean, obviously you you build a visual layer that makes it easier for people, you call it AI builders, to to create these type of agentic workflows. And and I think that expanded the market because this is not just a technical audience. Technically, anyone could be a builder. But I think you also mentioned something that I think is quite unique compared to other platforms, which is you allow the possibility to self-host your own product. What was the rationale behind making that decision? >> I think that that was very core incentive for the simple reason like when I started NLnet, I wanted to ensure multiple things. First, I really wanted to make sure that we have the and the possibility to become like the default tool out there. And if you want to become the default tool, you have to obviously make sure a lot of people can access to it and get access to it for free. Um and so I think that's why I always say especially like a lot of yeah, I think like I think that's that's a really a requirement to really kind of really get to that. The other thing is like I saw a lot of open source tools out there and I saw that they were able to build these amazing communities that really kind of made that success and this kind of um usage even just what possible in the first place. So, then again it was very clear I want to make um it available for free and again open source was one of the one of the directions I should do that. I went down a slightly different path very early on where I saw that a lot of the open source companies actually decided to kind of change the license and kind of change it to something more restrictive. And I saw that the community object got very angry about it less because of the license it shows mainly because they actually the company suddenly changed the rules. Said, "Here, first I I can do all of this." And suddenly take took like some rights away. And that really pissed people off a lot. And I thought, "Hey, I you cannot afford it like building up a a successful community is very hard. It's also like suddenly very easy to lose it." So, I thought, "Hey, I got to be very up front about it from the very beginning." So, I said, "Hey, I like make it available for free for everybody because I'm a good person. I actually want to build a business. I want to kind of pay my own salary at some point. I want to hire a lot of people that kind of provide value and I wanted to ensure that this money actually goes back in the company because I knew we kind of reinvested into our support, into our docs, in our product, and in doing quite great events. Only like this we can really kind of keep on growing and kind of make a build a successful company. So, from the very beginning I chose this little different license which we now we call it a fair code license. So, we say you can do more or less the same thing as you can do with open source. The only limitation you have is that you cannot commercialize the code. So, you can use it commercially so you can use it in your company at any kind of scale you want, but you cannot build a for example a hosted version of it and then charge other people for that. But we thought again it's it's a quite fair ask. That's why it's called the fair code license. And I think that's um we did So, from the beginning we thought about hey, how how can we enable the scale? How can we ensure we build a very great community can really build up the trust because I think trust is really the center of a community. What what's really important is like people can say a lot of things and they can also change their minds tomorrow, but only if you kind of are very consistent and from the very beginning people say see that the things you say you actually do and you not just saying one thing today and another thing tomorrow. You can really build up this very strong community. So it was very important to kind of really show that from the beginning and I think over the last 7 8 years people can definitely see that. >> I mean, I just went to your website and you have this big button that shows your GitHub repo with almost 200,000 stars and it's right there next to sign up. So it's a really I would say unique approach to first of all pursue this open source path with self-hosting option and still finding a business model that enables you to run a business while also making your product widely accessible to to people. I've seen that approach in other another companies but not in in your own specific space. Like we hosted the SVP of product from What is this database company? Well, a FiveTran just acquired DBT and and we we had them here and then MongoDB was still the example. But uh I think you're kind of the first mover in that automation. And by the way, how do you define your industry? Because I've seen terms floating around automation, orchestration, now loop engineering. >> Yeah, like we say we are more like orchestration layer. So like orchestration is probably how I would put it like an orchestration platform is how I would define it. That's how I would call it. But also back to the previous point, I think like a lot of open source products start as as as open source and they kind of are very proud about it and at some point they start to hide it. The source code is still there, but they kind of try to hide it more and more and kind of get people try to get people down the paid path and we are very deliberately say hey like we honestly like obviously we prefer if people use our hosted solution and actually pay us because obviously it allows us to reinvest the money, but at the same time like if people use us for free that's totally fine as well. Like there are just two different ways of using InfluxDB. And then the free one like the free one is a very important is actually super important for us like how we got adoption of all of these large organizations that we get used from anywhere from I don't know meta, Nvidia, Dell, Accenture, in Germany Voda- like Vodafone, Deutsche Telekom, Mercedes, like all of those companies. And we got adoption the same way as by people having a problem, starting with our free version and bringing it into this enterprise organizations. I think that is what what people really underestimate. I think very short-term, they think, "Hey, how can I ensure that this person pays me tomorrow for my paid hosted offering?" versus thinking long-term, I think making ensuring that these people have a very great experience to get a lot of value out of the product. When they get a lot of value out of the product, at some point they're going to become a paying customer. Even if not, like most ones will. So, I think like again, back to the adoption piece. >> Even if someone chooses the free option, I imagine you start connecting to different LLMs. And I know that you allow the possibilities to switch different LLMs. So, I guess there are open-source LLMs, non-open-source LLMs. So, how do you go about enabling those that optionality and still allowing people to, you know, pay when they have reached a certain level of usage? >> Like in the end, what what make us is different with N and N and total most other tools is like people bring their own key normally. Like we we have some functionality inside that you can get some some test credits. And we also working on a feature that you kind of kind of use this LLMs via N and N and kind of to also pay with them. But up to this point in time, people are bringing their own key. So, they say, "Here, I want to use an Anthropic model. I want to use an OpenAI model. Or I want to use something else via I don't know Hugging Face." And they bring their own key and connect it. So, kind of depending on the use case, depending on on how much money they have available, they kind of just decide by themselves which one they want to choose. But I think the very important thing and the powerful thing is also kind of you can switch later on. Like you can switch like it's probably like theoretically you can switch off off a second just with deleting one node and putting in another one. But obviously but at the same time, you still have to make some adjustments there where we also kind of have the functionality that people can create evaluations inside of N and N just to kind of test what does this change do and how do I have to adjust, for example, my prompts to make it possible, but again this is quite simple and much simpler than other platforms to actually change any model provider there from one to another, especially if it if you change for example I don't know use open open AI model by open AI or then hosted by Microsoft tomorrow. This lets you switch over a few seconds. >> Yeah, I mean there's all these chatter now, right? With the Chinese open source models performing apparently better than some of the US based models. I think clearly having the optionality for the user is an advantage. It allows you to not be locked in with any of the providers and ultimately you can see you are in the news a lot of the time when uh Anthropic or Open AI releases a new a new model. There's all these headlines. This company just killed 10 other startups, right? So I'm curious to know from your perspective, what is your market positioning and how you're thinking about uh the modes, but also like the relationship that you you have with these different uh providers. >> I think why why people choose like I think there's a definitely a lot of killing going on. I think like the thing killing a friend against a lot of um clicks. That's why I think the that happens literally daily that something kills kills another thing. So I would probably not take it too seriously. Most of the killed startups are are doing still very well. Obviously not all of them. So I think partly there is something to there. But then like what what makes Eden AI special is like our focus on kind of really audibility, like flexibility, um like the self-service ability and and the security piece and kind of really giving people really control. Like you can only you can see more or less like you can say the LLM is kind of the engine and and we are kind of the car and also kind of the the rules and the kind of the traffic the the whole the roads and and and the whole system. Like we kind of really connect everything and kind of be the kind of connecting tissue between all of them to to kind of make use of this kind of pile of different models and agents and business systems and bring it into kind of a extra process you can actually use. I think that is um is like we we we all need each other. We we need those those those model providers. The good good thing is you can replace them, but again you have a lot of models. You have a lot of different assistance you connect to and you kind of bring it all together and allow you not just to kind of build a nice demo out of it, but we we have a like a very big focus on actually building like reliable systems that are secure, that you can actually understand what's happening. So, if you build an automation with N and you know exactly how your data flows, that way it's using those tools, it's not going to do anything else. You see it when something goes wrong, what what exactly went wrong and I think that is again why especially large organizations choose us for business critical use cases because they can already get real ROI. They actually they can deploy us in production, not just for a nice demo out there. >> I think we've we've talked about the product enough. Now it's time to show it. And I love that you were the the one who volunteered to share screen and and demo what you got. So, Jan, I would love to see your product in action. >> So, in the past people built workflows themselves manually. But obviously with with things like cloud code and cloud code work, people realize that actually building can be much simpler and we also obviously realize it as well. We obviously want to give the great same great experience to people inside of N and also just allow literally anybody to build. Like I think a lot of feedback we always received is that N and was a kind of a more complex system, focused on more technical users. And the goal was always to kind of lower the bar and thanks to AI, we now finally able to kind of really empower everybody to build. So, what you can see here is our new AI assistant. We we just released it 1 and 1/2 weeks ago, but what it can do more or less see kind of describe what you want to do. Like we give you some examples. You can say, hey, I have a marketing use case. Um SEO and then you can just kind of define what what it should build out for you. I already prepared something because it takes um definitely a few minutes to actually build something. So, like here we have actually I just make it a little bit smaller. You can see better and kind of scroll up. Here I kind of just asked it to build me like a personal work play agent, um a kind of personal agent that kind of work with my emails with my calendar. And it starts with a chat trigger so I I actually talk with it. I told it what my email address is and then I told it like all the things it should be able to do here. Um and then I also made sure that again you can see with human in the loop that means like certain actions I want to ensure that it's not just a AI just does the stuff by itself. Like I don't want it to send an email in my name without me actually confirming that. So I just told it, "Hey, please ensure that certain steps can actually not be done unless I approve them first." Um so it then started building. A kind of um said it and then it told what uh what's going on and then it asked back a first question. It kind of wanted to know like um actually where was the first question? Uh here. It kind of asked like which model should use because it can obviously there's a lot of different models out there. I waited and simply said, "Hey, um we actually want I I saw it's on here. This is the first question." David then said for example, um "Hey, please um use Claude 2 or open router." Then it asked some other questions as well and you kind of see the answers down here as well how it should work. Then it kind of kept kept on building. Like normally you don't have to care about those things here but again for the more technical users actually want to understand what's actually happening there. They can actually open up here and kind of see exactly what's the kind of the AI system that it is doing and how it kind of starts to build its workflow. Then it was was building a while um and at some point it it said it's done but then I also said, "Hey, um actually the the way you currently build it um I want to get it changed slightly." Then it kind of went in there and and kind of made changes to the workflow and then in the end it was done and you can now see here you see like one of the artifacts which you can also see in the side is that the workflow that has been built. So you can go in here and let's also make it a bit larger that you can see it better. Um and that's the workflow that that the system built by itself. You can see like it's it's quite easily understandable. Like here you for example you have like if you start with a chat node here the whole conversation starts. You input the data. It flows into this agent. This agent by default uses um of Claude 3 5 but we also have it configured that as they're not you know Entropic is not always that reliable, we also have a four back model you can say, "Hey, if this model is currently not available, fall back to uh GPT uh 5.4 via open router." And then you see here all of the different tools we had defined previously. You can sort of list recent emails um or you can have like um it can get emails for you, it can even create draft and so on. And here we go we see the whole human in the in the loop steps. Then you can say, "Hey, it can send an email, but it can only do that if I get approval before." We kind of can see the configuration of each of those nodes by going in there. So, we can see here like here we say, "Bef- for approval, it kind of shows this message, send this email, please provide it before it goes out." It gives you like an overview of who's going to send it to, the subject, and the body. And so before it actually does that, it kind of does always the confirmation. We can see the same thing here as well for kind of sending a reply or for like creating calendar events. >> Mhm. In this In this case, so I'm seeing that two different workflows, right? Ones that could be fully automated and ones that require a human approval before they're executed. >> Exactly. Like the the end like at the end can totally exist in the background. You can say, "Hey, every time a new lead gets created um in in whatever system, create something in Salesforce for me." And it just does it totally automatically. You can also have like uh an internal chat system like this one I I'm showing off here where you can just say, "Hey, I create a personal personal personal assistant for myself." Which in this case kind of works through our chat like you can test in here or you can also use it for example via Slack or I can enable it via Telegram. Um or you can have in have something exposed to to your users. We have for example uh a big online travel agency which kind of automated a lot of the support actually via an AI assistant built within it in. They literally like 75% of the requests of the customers go through that agent where people can change uh hotel rooms, change bookings, can ask questions, and so on. Um and again, the nice thing about that example is even that the people that used a AI assistant like this AI customer representative, people actually seven like actually happier with with that than with a human um um assistant there, which is obviously amazing, which shows AI is not just about kind of lowering the cost and so on. It's actually providing also really use real value for the users. They can literally literally again anything from fully automated to literally everything in it probably can build anything. Got it. >> And just to clarify also on the this visual graphs was created automatically based on the configuration that you did before, right? >> Exactly. That was built automatically. Um we actually we did actually normally you have to kind of sometimes select for example like which credentials to use. To give you an example, here we for example a cloud sonnet, we can say here use the internet building credits or you can say here use my own API key. And you can do this can could do it with literally any tool here. You can say hey yeah I it's connected to those credentials of of my Gmail account and so on. So you can then still you can build it automatically and then you can say hey I can actually now go through and and make sure I can understand what actually has been built because I think the the problem with a lot of if you write code code like you have normally 10,000 lines of code that you hope does the right thing, but actually kind of reading and understanding them is almost impossible because it takes you honestly more time to partly understand it than actually to kind of create in the first place. And here it's just very simple. You can literally say here here I have a chat node. I I get a message here for example. You can say show me my last five emails. Then the data flows through here and then you can see ah here now I have this AI assistant as a AI agent. And you can see here now it's a system message that tells me hey I'm I'm an assistant, I have those tools, I have like certain rules in place. Here I get the the chat message and then I call I have access to those different tools. And you can very easily add more tools like this. There you have a you can even add code tools. You can have like you can do actions in in in different apps. Like you can see a lot of them. Anything you can probably your heart is asking for. >> Question about those those tools. So I saw on your website you have over 500 integrations with with tools. And so how do you go about that? Is it something you have to set up one to one with each of those tools? Or is there a more of automatic way for you to create integration? >> In the end like we have different kind of integrations in multiple levels. They're like certain built-in integrations that that we created ourselves, we're maintaining ourselves. This is for example like all the most important ones like for example anything with Google for example or Salesforce. We created them and we maintain them. Then there's also like custom like community integrations. The community built them and then we check them and make sure they're actually secure. And then people can also access them on their cloud solution as well or can manually install them. They're actually a lot more than those 500 out there. And then even if there is no integration built by us and no integration built by the community, you can still kind of connect to the system manually via an HTTP request in order to kind of say make a get or post request to a certain endpoint. You can also ask the AI assistant to build it for you as well to kind of configure the nodes for you. But in the end you should be able to connect to literally any kind of API based system out there. >> Yeah. I remember the first time I set up my OpenGl again it was quite an ordeal. I mean and I'm very really technical but still there was no visual layer. So here I can imagine you can choose to drag and drop and and set it up. And I guess you also don't need to buy a MacBook or a Mac Mini, right? >> No no no it's running totally in the cloud. You can also just like test very easy. It's literally so see what's going on. If I say say now for example let's say I can say send an email to James at I don't know email.com saying hello. And then I can literally see here how it's actually going through the data. The message got received and now the agent starts building. It kind of adds data to the memory. It calls out to the model, and then it kind of tries to figure out what to next. And then you can see that it's going to call one of those tools actually going to answer. In this case, it said, "Hey, send out this email. Please review before I send it out." So, you actually see it went here, and because we said, "Hey, there's actually a human in the loop." It actually stops now and it kind of keeps on waiting till it actually says it should send it out. So, I could now say, "Send it." Or "Don't send it." "Send." Or "Cancel." And you can see that would go on or actually stops here. So, like it's again you can And if something would have gone wrong, you would see like a notice red here, and you can just very easily debug. And again, now you can also just go in here and see, "Hey, what actually happened?" You can see the logs. I got the input, I got the model, I I sent this kind of input to it. Then it kind of called the tool with that kind of data. It kind of kind of asked for approval, so you can literally go through the whole to to everything that happened very easily and kind of debug and understand what if it actually does the right thing you wanted to do. You can do that for literally any execution that happens. >> So, as you know, loops are hot now, right? This whole concept around Now you can train your own agents, or you can set a goal and they will self-improve. How do you think about that, and how is it possible for people to train their own agents as they execute some of these workflows? >> Like that what what happens there is is a kind of loop like you you call the agent, it calls a tool, it goes back to the agent like to the model, it calls another tool, it can respond to the user, and then can can go back here again. You can also literally can like you can literally loop like literally can just tell it to kind of talk to itself if if I want to by just adding this connection here. And it is very powerful. You can build very complex systems there. I can just say here something happens there, then actually send do something with it output, send it to another agent, then I can use it again as an input. Like people can build really really complex use cases. And also like maybe actually let me share this one here. This is our website. You can see here templates. And you can see there's like over 10,000 templates that our community created. So you can say here I want to use something like a sales in sales for example. You can see here um nice things people built. Or you can say here I want to have something with I don't know Google Sheets. And you can see like a nice example how you kind of auto create TikTok videos or anything else out there. And then you can kind of click there and they kind of can see here literally the actual um automation that actually has been built. They kind of already explored. So like the same way you can like like just explore it. You can see here what's actually happening. How did it get set up? And then you can literally just say I want to use it. You can copy it. And then you can just literally copy it um in here. So let me just um start a new one. Just create an new workflow. Now I just literally just copy paste. And you can see the whole thing is in there. And now I can kind of configure it with my own settings. For example, now here in this case it's it has an OpenAI model. So I could say I can use my own credentials or use the OpenAI ones. So you can Oh, sorry. I didn't I just forgot. I had forgot to share that I had to do >> Yeah, I I get it. I think this is this that goes to the important problem we see in the product, which is the starting with a blank slate sometimes is hard. So having some sort of recipe that allows you to validate what you need and you still have the ability to to edit it, right? Based on the tool stack that you have or the specific use case that you need. >> Exactly, yeah. And um maybe also to finish the the previous example, I think like um I said I said before like we had our assistant here and and it it again now it built it out. Like and I have now this workflow here. But I can actually as well like can actually now say hey that it's great what you built here, but I actually want to kind of extend it. So I can for example say now they have the existing workflow already, but now to add for example this like calendar tool where I want to book my one-on-ones with people. I want to have consistent formats. They have like always 30 minutes and so on. And now I can just send it to the agent. And again, now I can see like how it actually works. Like also way it it does some thinking and then it kind of will start to extend the existing workflows. You can say hey you can start very simple and you kind of not you don't have to kind of build and like go totally crazy and kind of come up with the whole solution from the beginning but actually can also do it iteratively. You can say hey start with the most basic version and then I want to add this this other things in time as well. Now I can see now it's it's working here and now it kind of it it does some reasoning there. It kind of goes back and thinks about what it should actually be doing. It will probably going to take like a few minutes. It's a good addition. I need a Google contacts tool name. Again it figures out kind of the different nodes it needs. It looks up if credentials already exist for example and says hey I need Google contacts credentials. It says hey I already have an existing one so I can actually connect to to to Google already and then it will hopefully be done in in less than a minute and then you can really see that it extended the existing workflow as well. And again that's and the amazing thing is that what what what to show here is that again literally everybody can do it like because they have to know what you actually want to build and if you know what you want to build you just describe what you want to have done and it goes out there and it actually does it for you and again and then you can just again literally test it by yourself and and and show it it actually works in the right way and then it can very easily deploy it in production and kind of use it on a daily basis. >> I want to ask you a question about the main bottleneck that I see these days with this type of assistant. So people are able to now create their own they call it second brains or AI chief of staff or assistant that seems to work really well for their own productivity. But the multiplayer bottleneck is real right? Like how do you now create a shared environment where people can benefit from the work that others have done and they can really collaborate instead of creating their own versions of their own assistant. >> And then like we just I think I'm sure if you released it yet, but it should be closely if shortly before releasing is where instead of kind of just creating your own, like connecting your own credentials, you can actually say, "Hey, assign this credentials dynamically." So, in this case, if I interact with it, it would use my credentials and would access my Gmail account and my calendar. If you would actually chat with it, you can give it access to your calendar and they can do it for you. I think that this is very important piece where you can trust again, every if you've done 10,000 people in an organization where you have 10,000 people building something separately, you can actually have literally anybody just having one of them deployed and then the whole organization gets access to the same thing as well. And generating workflow, I said, "That's fine that they can do that." And I think that's this is very important piece as well. >> Totally, cuz ultimately, we got a super agent manager, right? Like you have all of these different agents doing different things. Sometimes those agents are yours, sometimes it's your teammates, and so, what ends up being the the control panel, kind of the the surface for the product leader to really understand what's going on and be able to make any type of modifications? >> Yeah, I agree that is so important. Also, by the way, here you can also already saw maybe very fast, not just spinning the workflow, it's actually kind of already kind of tested it for you as well. So, that actually you can be sure that when you build something, it it kind of it is already it's in the in the working state. >> Really cool. And the other thing that I that I noticed it's it's becoming challenging for especially large organizations is the deployment into production. I like these these type of demos work very well when you have full control over the tools you're trying to connect to, but as more people join the party, then you also have more controls and checks, right? So, and it we're talking about large enterprises that already use and and like how were you able to get over all of those procurement and guardrails problems to make sure that they trust your solution? >> First, like we talked about before, self-hostability. I think that's just an important piece already that people actually know where where it actually is running. Um and they can also very often they kind of use their own local models, for example, to actually deploy N and N. Um and then other thing the other piece is like literally like if you build something with with with AI, like it can go rogue very easily. Again, for example, I don't know, it deletes data it shouldn't delete or it sends out emails it doesn't what shouldn't it shouldn't send. But again, because we make it very simple, like literally this agent can do only those things. It cannot go rogue. You have exactly specified all the things it can be doing. Again, also the things it can only be doing once actually a human approved. So, it means like before those things can go in production, somebody else can very easily review it again. And they don't review again 10,000 lines of code to actually review a workflow that they can easily understand and go through and kind of know exactly what it's going to be doing. But I think that's just makes it very easy for these enterprise organizations to not just again have a nice demo, but actually kind of release it in production much faster than other systems. >> I think this is a very common challenge for a lot of companies that maybe started more on the SMB segment or direct-to-consumer people who are like really curious and building something and then they're bringing it to their enterprises. So, in addition to just proving that you have the right security checks, like what does go-to-market motion look like because PLG can only take you so far? >> First, like we got into the most organizations like via bottom-up PLG growth. And interestingly, it's not just bottom-up, we got into a lot of into a lot of organizations as well, literally top-down as well, where people on the board, where C-level, I don't know, the CTO or the CEO, whoever saw content around N and N and just was one like, "Why don't we do the same thing in our organization as well?" And they kind of bring it into organizations and then very often find that they're actually already using N and N just in a more limited way. And then it makes it much much easier than to actually do that. Um at the same time, obviously like at some point you want to accelerate things. Um another nice way to accelerate things is definitely partners. And I think that is one thing that is also works very well for us. Like we have a lot of we talked about the community before like a lot of the community consists of people that have agencies that build up agencies around and at the end it really kind of not just again they go out there and say, "Hey, I could I can actually help you to build this workflow. I can actually maintain it for you." Um I I help you to kind of even figure out what can can be automated. They're kind of this this accelerator. We also work with For example, just a month ago we announced a partnership with Accenture as well. And we talked with other system integrators as well. There other partnerships getting announced very soon um on top there. So that we're looking for this kind of accelerators that can really help us to kind of reach more of our user base and and therefore reach more of the opportunity easier because we can obviously not have a one-to-one relationship with everybody. So we we have to ensure to actually kind of use all the channels that are actually available to us. >> So as you partner with these companies, consulting firms, and others as your implementation partners or FDEs, call it however you want, it seems like one of the challenges that these companies have and by the way, we do that for a living as well is that you get the you do the initial sprints with the client, they they see the magic, they love it, but at some point you want them to be more self-sufficient. They also want to be more self-sufficient, right? So I'm I'm thinking about the actual adoption curve. Like maintenance, like what is the approaches that you are seeing now for this type of large enterprises? Is it like having their own center of excellence or chief AI officer that is overseeing all the AI implementations? Do you see more functional leaders trying to handle their own automation? Like what is your your vision on that? >> We we definitely see all of it theoretically. Like all of it is out there. Um but what like just the the thing they're doing internally as well that we um suggest for our partners as well is not just not having it only centralized. Like I think that you can definitely get if you have a centralized team that builds automations or AI assistants or agents in whatever way for other people like you can reach like I think you get a lot of value. But you don't really empower people. You build for them and again it's at some point it becomes unmaintainable because you suddenly like this this this department literally has to kind of maintain hundreds of thousands of of automation. It doesn't work. It's also the opposite of why I created N to N. Like the reason why I created N to N is was because I saw that the people that had the problems were dependent on other people like myself who actually helped them to kind of build things to build automations or or build agents. And at N the idea with N to N was that the people that have the problems can solve it themselves. Because again this is why we we try to make it simpler and simpler and simpler. So like this again we have an an AI and automation department internally as well. But what they're supposed to be doing is like still help like the idea is about empowering people inside of the organization. Again the people that have a problem should be empowered to build it themselves. They're still there for you to say hey I don't know how to do this one thing then they help you out there. Or they help you kind of try to figure out first what is actually possible to build. They kind of trying to kind of the idea is kind of help the people to fish because in the beginning they have literally no idea what's actually possible. And they're kind of getting started with something small and kind of empowering them more and more. That's how we really see how we can really build it up successfully and then obviously document what they did. Kind of ensure you have to kind of the right the right tools in place but it we kind of see them as kind of creating the guardrails kind of they they they own N to N their own everything around it their own education but the building should be owned by the people that actually have the problems because they're like nobody else can do it better than them. They actually know the whole process by heart. And explaining it to somebody else is just super inefficient. It's never going to be the same thing and for literally any problem you have again any simple change you have to go back to another person again that has the same context and it it's just super inefficient. >> Yeah. And and that exactly and that is why it's thing is so complicated to measure ROI. right? Because first you of course need to understand what's possible. And once you taste that magic, it's hard to go back. So, adoption seems to be a good way to get started. But at some point, people start questioning, okay, but all of this productivity goes where? Right? But at the same time, you can't fully give a one-size-fits-all answer to all the companies. And here's how you measure ROI of your AI implementation. So, curious to know what is your take on how you're seeing the companies that are more advanced kind of take that AI option metric a little further down the line. >> I think that's I think that's definitely also something we struggled with as well. That's something because there's so many like I said, so many different ways to actually measure like the the impact. And again, like we we also started the initially thought, hey, that's the simplest thing that you can also do very easily with and then it's literally just track like how much time it actually saved. But I think that's actually not not not not the best measure. We kind of realized it's also like very easily kind of you can just cheat very easily in a kind of in certain automations like even though they run every minute, it's not just and and it would take you 10 minutes to run it every time manually, it doesn't mean like it it saves you crazy amounts of time there. So, like what we actually now looking I think one thing is is the ROI piece, but what we are kind of in in actually internally, we care less about the ROI kind of measuring the right way because we just know that it's like it it it has a lot of impact. What we actually want to drive forward and want to ensure is that actually change is happening. That actually every department actually thinks about like what can they be doing to kind of drive change forward. How can they make sure that they actually not that they think differently, how they kind of have more impact. And that's why each department has to define like their AI vision and kind of really kind of define the certain things they want to achieve every quarter. And then we as a company ensure that each that each department kind of reaches those goals and we as a whole company actually reach at least 90% of those AI targets have been set. I think again, you can measure anything. I think. Again, also I think it's not one thing one size fits all. And maybe for some of them it makes sense to kind of check again of how much time is saved or how much money is saved, whatever. I think you have to decide it yourself, but I think honestly, the most important thing is right now certain things you can take as a given. You know it provides impact. And then you just have to kind of think it's still want to make sure like I think it becomes then problematic when you have like on one side an unknown outcome and on other side you have a lot of a high very high spends. Because then it is not very clear anymore. And I think that's again why people appreciate and it end very much is because the cost of running an AI assistant built with an end is much lower than in other systems because we're not just saying, "Hey, AI solution for everything." We say AI is one piece of it. Again, what you want, you need AI, but at the same time you still want to have deterministic logic. In this case, for example, the step approved those emails or I don't know, do X or Y depending on a certain number. That's a deterministic logic. Like it doesn't need an AI to actually do that for you because AIs are expensive. If you just figure it out, they're not very reliable and they're very slow. Like why would you do it with an AI? And then you still have to have the human in the loop again, which again ensures that you cannot just use it for a nice demo, but you actually can use it for a business critical use cases because you know the AI cannot do anything it's not supposed to be doing. And I think that is again why that the cost of running an AI system is actually in built in and that is actually quite low. And then over that the ROI um is very clear again. In the most simple form, start with just and start to track time saved. And then you can actually go further and kind of literally kind of track your own things like in an end you can even do it dynamically. You can just say, "Hey, I have this this arbitrary metric." Um and again, for each execution save that number. It would maybe get saved as time saved, but you can literally kind of use it for anything you want. Just to make sure it has to kind of the impact which actually organization is aiming for. And again, one last thing I want to say there is the example I gave previously with this kind of AI assistant for this online travel agency, I think people like I think this ROI thinking is one thing is great if you want to have ROI, but very often like the advantage is not just money saved or anything like that. Very often it's also better customer experience as well. Again, people prefer the AI assistant. Why? Because never gets tired, it can speak any language, it works 24/7, it has the perfect context. Like that's great. And yeah, and I think that is this other thing is like certain things it's not just normally one value that certain things you can drive forward and say, "Hey, maybe I don't want to set about saving money, it's about having better customer experience or it's about saving money." I don't know like whatever your your your company is driving forward, you should measure that one thing. >> I like that cuz sometimes with the introduction of new new tools it seems like we're forced to now coming up with 10 new metrics to measure the specific impact of that new tool into our business, while in reality it's all intertwined that it's just hard to isolate a specific variable. So, using the same business metrics that your business uses to measure productivity, outcomes, performance, ultimately I think is the easiest and most reliable way to see if okay, how you're getting there is actually helping or not. But, um, that's at least also how I try to measure the own impact of our own AI implementations in the business. Like, okay, are we seeing a NPS of the customer go up? Are we seeing revenue grow up faster? Then great, it seems like what we are doing in the house seems to work or not. But, it's like if you go to a restaurant and order a chicken, if you like the taste of the chicken, who cares where that olive oil is coming from if it's AI or non-AI? >> Exactly. And I think like the thing is like you can even say very often it's maybe the right thing to actually spend more money because of AI because like what you care about is for example speed. It's like if a it's a difference if I have a human that does it and it takes him for example 15 seconds, I can do with the AI which does it maybe in three seconds. Like maybe that's worth paying five times that, but it really depends on your own business, um, and what what is important for you. >> Jan, it's been a pleasure to have you on the podcast and see you demo, to you also explain the product, go-to-market strategy, and thank you so much for your time. >> Well, thank you for having me. That was great.