How to Know Your AI Feature Actually Works (n8n's Founder's Metric) | with Jan Oberhauser, CEO n8n

0:00I 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. Obviously we prefer if people use our hosting solution and actually pay us because it allows us to reinvest the money. But at the same time, if you use us for free, that's totally fine as well. There are just two different ways of using NNN. Jan Oberhauser, CEO of NNN. What made us special is very early on, really focused on the community, very heavily created an amazing platform, but it also really ensured those people had a great experience in there like they support each other how is it possible for people to train their own agents as they execute some of these workflows that is very powerful you can build very complex systems there i can just say here something happens there then actually send it do something with it output send it to another agent then they can use it again as an input and people can build really really complex use cases how you've seen the companies that are more advanced kind of take that ai option metric a little further down the line.

0:53AI cannot do anything it's not supposed to be doing.

1:23Tackling 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? Hint, not the product theme. The metric he uses to know if an AI feature actually works. How one company routes 75 % of support through an N8N agent with happier customers. Let's get into it. Welcome to the product podcast, Jan. Thank you for having me. Great to be here. I have 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 and then i 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 knew it's not going to make or break the company so it's just that okay like i find the 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 of the good domains are always gone so one name was left which was notemation the idea was there's kind of it's a node-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 had the same like kubernetes k8s n then eight letters n this is the same thing actually and then n it and stuck um ended and that i was available after the seat round i could buy ended and.com for 5k as well we don't use it right now but at least we own it to make sure it doesn't get more expensive uh and now everybody has to say that strange tongue breaker And there's so many different ways of saying NNN, which I didn't even imagine, like from like Nation, Nathan, Nathan, BFN, Otcher, N8N in Germany, like there's like a million different ways.

2:58It's quite exciting to see what people can happen. You made it work. Like people recognize the name. It's obviously not very common, but still, you know, like the product works. People will find a way to remember it. You started this journey over seven years ago, right? Way before LLMs became mainstream. So I'm very curious to, but you should really, I started 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 decided to pivot into that? It's just that we started pre-AI and honestly, I didn't imagine at all, we're going to ever do anything with AI at all.

3:29But then obviously, JetGPT came along, we saw like a large language models getting kind of strong and strong and better. And so we just were wondering, like, how do we ensure that like NN is not just part of the, like, it's not just a future for NN, but we actually participate 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 our competitors doing? And all of them, including ourselves, honestly, we just sprinkled some AI on top. 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 NN, but actually people building AI part applications, agents with NN.

4:06And that's when we then realized that actually NN 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 here from google drive this is from your local files is it from salesforce or whatever it does something with the data it transforms it takes a decision and then it outputs this data again again into a system or writes it or 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 and we really kind of are able to kind of create something really amazing and powerful and not just kind of allow people to build simply automations, but really powerful agents as well.

4:51Obviously, that vision makes sense in retrospect, right? Because we're all using agents and understand now the concept of agent 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 the U.S. And you guys are, you announced you crossed over$40 million in annual recurring revenue. You had massive funding rounds at a latest one, a$2.5 billion valuation with participation from NVIDIA. So there's definitely something unique there. So I'm curious to know what was that kind of unique thing or secret sauce that helped you be on the map with this incredible traction?

5:30Sure. First, to clarify, we actually over 100 million euros ARR already. and we have already$5.2 billion right now with kind of announcing our partnership with SAP. So we are growing still nice. We don't announce the exact revenue numbers, but just people, we crossed 100 million ARR a while ago. And sorry, and the question was like, what enabled that? Did this expect growth? By the way, I love to be wrong about those things. Like, oh my God, now it's over$100 million ARR. So there are so many tools out there that were trying to do something similar. In fact, you were in the first mover, right?

6:04There's so many other platforms like Zapier and Make and others that were, they already 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 in early days, what helped to get that initial tailwind? I think like from the very beginning and the end was all about like power and flexibility. Like I think that's what we 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 actually wanted to get a bit and bring it in production or you actually kind of wanted to scale it, kind of this platform really makes out.

6:38That's why we kind of made a decision to kind of really focus on power and flexibility and kind of really ensure people don't outgrow our solution. And I think what made us special is like this focus, but also our focus on a community where we early on kind of really focused on a community very heavily and kind of created an amazing platform, but it also really ensured those people had a great experience in there, like they supported each other. We supported them. We provided great support. We had online events. Now we do offline events. And we just making sure like putting those builders, which is really at the center of it, and it is about empowering people.

7:12And even our kind of, one of our core company values is we are builders. Like we are builders, our community are builders, our users are builders. I think like having like this kind of, this alignment between everything kind of really ensures that you kind of attract the right people and it really kind of provided value. And I think especially that in combination, I think we grew very nicely the first years, but when it really took office, when it then focused on AI, as you mentioned before, 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 powerful AI capabilities like we had them.

7:46like 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 API somewhere but you actually build like proper agents like agents anything from memory you can have like multiple models you can have different tools you can configure them very easily you can add scripts and the kind of this kind of power and flexibility that was nowhere else available I think that's what people really craved in combination, obviously, of other advantages, like, for example, you can self-host us.

8:21Like people really appreciate that just that they know where that data actually lies, knowing they can run it behind a firewall, they can scale it. And like we have a big focus on reliability, scalability, and auditability, I think, where you can just very easily understand what's actually going on there, even in more complex flows. I want to unpack some of the concepts you mentioned, because you're right. I mean, obviously you build a visual layer that makes it easier for people, you call it AI builders, to create this type of agentic workflows. And I think that expanded the market because this is not just a technical audience.

8:55Technically, anyone could be a builder. 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 it was very core incentive for the simple reason. When I started N &N, I wanted to ensure multiple things. First, I really wanted to make sure that we have the possibility to become the default tool out there. And if you want to become the default tool, you have to obviously make sure a lot of people get access to it and get access to it for free.

9:25And so I think that's why I always say especially a lot of... I think that's really a requirement to really get to that. the other thing is like i saw a lot of open source stores 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 usage even just possibly in the first place so thank you for it was very clear i want to make it available for free and again open source was one of the one of the directions they 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 obviously got very angry about it less because of the license it shows mainly because they actually the company suddenly changed the rules said hey first 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 you cannot afford it like building up a successful community is very hard it's also like sadly very easy to lose it so i thought hey i'm gonna be very upfront 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 a company because i knew we kind of reinvested into our support into our docs in our product and doing quite great events only like this we can really kind of keep on growing and kind of make a bit of a successful company so from the very beginning i chose to split different license which is now we call like a fair code license so we say you could 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 a company at any kind of scale you want but you cannot build for example a hosted version of and then charge other people for that but we thought again it's a quite fair ask that's why That's called the Fair Court License.

11:19And I think that's, we did, so from the beginning, we thought about, hey, 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 for community. What's really important is like people can say a lot of things and they can also change their mind tomorrow. But only if you kind of are very consistent and from the very beginning, people say, see that the things you say actually do and 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.

11:48And I think over the last seven, eight 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 a self-hosting option and still finding a business model that enables you to run a business while also making your product widely accessible to people. I've seen that approach in other companies, but not in your own specific space. Like we hosted the SVP of product from, what is this database company?

12:24Well, Fivetran just acquired DBT and we had them here. And then MongoDB was the other example. But 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 the orchestration layer. So like orchestration is probably how I would like an orchestration platform is how i would define it that's um however you call it but also back to the previous point i think like a lot of open source products start 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 very deliberately say hey like we honestly like obviously we prefer if people use our hostess solution and actually pay us because they obviously allow 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 in and in and the free one like the free one is a very important it's 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.com i see this 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 that into as enterprise organizations.

13:42I think that is what people really underestimate. They think very short-term, think, hey, how can I ensure that this person pays me tomorrow for my paid hosted offering versus thinking long-term, 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. And 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 is to switch different LLMs.

14:13So I guess there are open source LLMs, non-open source LLMs. So how do you go about enabling that optionality and still allowing people to pay when they have reached a certain level of usage? Like in the end, what makes us different with NNN and most other tools is like people bring their own key normally. Like we have some functionality inside that you can get some test credits and we also work on a feature that you can kind of use this LLMs via NNN and kind of to also pay with them. but up to this point in time people really bring their own keys so they say hey i want to use an entropic model i want to use an open ai 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've available they kind of just decide by themselves which which one they want to choose but i think the very important thing and the power of anything is also can you can switch later on like you can switch like it's probably like theoretically is a switch of 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 it's quite simple and much simpler in other platforms to actually change your model provider there from one to another especially if it's if you change for example i don't know use open an OpenAI model by OpenAI or then hosted by Microsoft tomorrow.

15:35Let's see a switch off for a few seconds. Yeah, I mean, there's all this 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 the news a lot of the time when Anthropic or OpenAI releases a new model. There's all these headlines. this company has 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 the modes, but also like the relationship that you have with these different LLM providers?

16:12I think why people choose, like there's definitely a lot of killing going on. I think like I think killing apparently gets a lot of clicks. That's why I think that that happens literally daily that something kills another thing. So I would probably not take it too seriously. Most of the killed startups are doing still very well. Obviously not all of them. So I think partly that there's something true there. But what makes LLM special is like our focus on kind of really audibility, like flexibility, the self-hostability and the security piece and kind of really giving people really control. Like you can normally, you can see more or less like, you can say the LLM is kind of the engine and we are kind of the car and also kind of the rules and the kind of the traffic, the whole, the roads and on the whole system.

16:54We kind of really connect everything and kind of, we are the kind of connecting TCP We trust all of them 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 process you can actually use. I think that is like we all need each other. We need those model providers. The good thing is you can replace them. But again, you have a lot of models. You have a lot of different systems you connect to. And we kind of bring it all together and allow you not just to kind of build a nice theme out of it. but we have a very big focus on actually building reliable systems that are secure, that you can actually understand what's happening.

17:30So if you build an automation within an end, you can know exactly how data flows that way. It's using those tools. It's not going to do anything else. You see when something goes wrong, 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 can deploy us in production, not just for a nice demo out there. I think we've talked about the product enough. Now it's time to show it. And I love that you were the one who volunteered to share a screen and demo what you got. So Jan, I would love to see your product in action.

18:02Sure. In the past, people built workforce themselves manually. But obviously with things like Cloud Code and Cloud Cowork, people realized they actually built and can be much simpler. And we also obviously realized it as well. We obviously want to give the same great experience to people inside of NNN and also just allow literally anybody to build. Like I think a lot of feedback we always received is that 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're now finally able to kind of really empower everybody to build.

18:33So what you can see here is our new AI assistant. You see, we just released it one and a half weeks ago. But what you can do more than see, you kind of describe what you want to do. Like we gave you some examples. You can say, hey, you have a marketing use case, SEO, and then you can just kind of define what it should build out for you. I already prepared something because it takes definitely a few minutes to actually build something. So like here we have, actually I'll just make it a little bit smaller. It can see better and kind of scroll up. Here I kind of just asked it to build me like a personal workflow agent, a kind of personal agent that kind of works with my emails or with my calendar.

19:08And it starts with a chat trigger so I can 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. And then I also made sure that, again, here you can see with human in the loop, that means like certain actions I want to ensure that it's not just AI tested 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. So it then started building. I kind of said it understood what's going on.

19:41And 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 it should use because it can obviously there's a lot of different models out there uh very then simply said hey um we actually want i saw it's on here this is the first question the video said for example um hey please um use cloth through open router then it asked some other questions as well you kind of see the end system down here as well how it should work then it kind of kept on building like normally you don't have to call about care about most things here but again And 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 the kind of the AI system is doing and how it kind of starts to build that workflow.

20:24Then it was building a while. And at some point it said it's done. And then I also said, hey, actually the way you currently build it, I want to get a change slide. Then it kind of went in there 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 see on 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. And that's the workflow that the system built by itself. You can see like it's quite easily understandable. Like here, for example, you have like if you start with a chat node and here the whole conversation starts, you input the data, it flows into this agent.

21:03This agent by default uses kind of Claude Sonnet 5. but we also have it configured that as they're not you know anthropic is not always that reliable we also have a fallback model they can say hey if this model is currently not available fall back to a gpt 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 or you can have like 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 where you can say, hey, you can send an email, but it can only do that if I get approval before.

21:39We kind of can see the configuration of each of those notes by going in there. So we can see here, like here we say, 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 creating calendar events. In this case, I'm seeing that two different workflows, right? Ones that could be fully automated and ones that require a human approval before they are executed.

22:12Exactly. The end can totally exist in the background. You can say, hey, every time a new lead gets created in whatever system creates something in Salesforce for me, and it just does it totally automatically. You can also have an internal chat system like this one I'm showing off here, where You can say, hey, I create a personal assistant for myself, which in this case kind of works through a chat like you can test it in here, or you can also use it, for example, via Slack or via Telegram. Or you can even have something exposed to your users. We have, for example, a big online travel agency which kind of automated a lot of the support actually via an AI assistant built with NNN.

22:53But literally like 75 % of the requests of the customers go through that agent where people can change hotel rooms, change bookings, can ask questions and so on. And again, the nice thing about that example is even that the people that use their AI assistant, like this AI customer representative, people actually happier with that than with a human 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 real value for the users. They can definitely get anything from fully automated to literally everything needs approval.

23:30You can build anything. Got it. And just to clarify also, this visual graph was created automatically based on the configuration that you did before, right? Exactly. It was made automatically. I mean, 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, Cloud Sonnet, we can say here, use the NNN built-in credits. or you can say, hey, use my own API key. And you can do it with literally any tool here. You can say, hey, it's connected to those credentials of my Gmail account and so on. So you can build it automatically and then you can say, hey, I can actually now go through and make sure I can understand what actually has been built.

24:11Because I think the problem with a lot of, if you wipe 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 the party understand it actually to kind of create in the first place and here it's just very simple you can actually say here here i have a chat node 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 an ai agent and you can see here notice 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 chat message and And then I call, I have access to those different tools.

24:51And you can very easily add more tools. Like there's, you can even add code tools. You can have like, you can do actions in different apps. Like you can see a lot of them. Anything you can, probably your heart is asking for. There's your question about those tools. So I saw on your website, you have over 500 integrations 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 it a more of automatic way for you to create integration? In the end, there are different kinds of integrations in multiple levels. There are certain built-in integrations that we created ourselves, we maintaining ourselves, which is, for example, or the most important ones, like, for example, anything with Google, for example, or Salesforce.

25:34We created them and we maintained them. Then there's also like community integrations. The community built them and then we checked 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 node. You can just say make a get or post request to a certain endpoint. You can also ask the AI system to build it for you as well to kind of configure the nodes for you.

26:07But 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 OpenClock agent, it was quite an ordeal. And I'm relatively technical, but still, there was no visual layer. So here I can imagine you can choose to drag and drop and set it up. And I guess you also don't need to buy a MacBook or a Mac mini, right? Well, it's running totally in the cloud. You can also just like test it very easily. Literally see what's going on. Say now, for example, I can say send an email to James at email.com saying hello. and then now you can literally see here how it's actually going through the data and the message got received and now the agent starts building it kind of adds data to the memory it calls out to the model then it kind of tries to figure out what to do 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 it 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.

27:13So I could now say send it or don't send it, send or cancel. And you can see that it would go on or actually stops here. So like, if something would have gone wrong, you would see like a node is 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 inputs, I got the model. I sent this kind of input to it. Then it kind of calls a tool with that kind of data. It kind of asks for approval. So you can literally go through the whole to everything that happened very easily and kind of debug and understand what if it actually does the right thing you want it to do.

27:49And 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 so 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 then what happens there is a kind of loop, like 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 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 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 put, send it to another agent, then I can use it again as an input.

28:38Like people can build really, really complex use cases. And I also like maybe actually let me share this one here. That's our website. You can see here templates. You can see there's like over 10 ,000 templates that our community created. So you can say, hey, I want to use something like a sales. In sales, for example, you can see here nice things people build. You can say, hey, I want to have something with, I don't know, Google Sheets. and they 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 automation that actually has been built they kind of already explored here like the same way you can like just explore that 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 in here.

29:27So let me just start a new one, just create a new workflow. Now 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 has an OpenAI model. So I could say I can use my own credentials or use the NLN ones. So you can, oh, sorry, I just forgot. I forgot to share the right step. I get it. I think this is, this tackles a very important problem we see in product, which is starting with a blank slate sometimes is hard. Exactly. So having some sort of recipe that allows you to validate what you need and still have the ability to edit it, right, based on the tool stack that you have or the specific use case that you need.

30:06Exactly, yeah. And maybe also to finish the previous example, I think like I said before, like we had our assistant here and again, now it's 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 actually want to can extend it so i can for example say now do you have the existing workflow ready but now add for example this like calendar tool where i want to book my one-on-ones with people i want to have a consistent format they have like over 30 minutes and so on and now i can just set it to the agent and again now i can see like how it actually works now it goes away it does some thinking and then it kind of will start to extend the existing workflow so 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 and iteratively you can say hey start with the most basic version and then i want to add this this other things um in time as well now i can see now it's working here and now it kind of does some reasoning there it kind of goes back and thinks about what it should actually be doing i will probably kind of take like a few minutes it's a good addition i need a google context tool name again it figures out kind of the different nodes it needs it looks off if credentials already exist for example it says hey i need google context credentials it says hey i already have an existing one so i can actually connect um to to google already and then it will hopefully be done in in less than a minute and then you can see that it extended to the existing workflow as well and again that's an amazing thing is that what i want to show here is that again literally everybody they can do it like because you 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 actually does it for you and again and then you can just again literally test it by yourself um and and ensure it um it actually works in the right way and then they can very easily um 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.

32:12So 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 the trust, I think, I'm not sure if you released it yet, but it should be shortly before releasing, is where instead of kind of just creating your own, like connecting your own credentials, you can actually say, hey, assign these credentials dynamically.

32:50So 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. So I think that is a very important piece where you can adjust. Again, you have 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. You can generate a workflow. I said, that's fine that they can do that.

33:21And I think that's a very important piece as well. Totally, because ultimately you become a super agent manager, right? 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 control panel, kind of 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 super important. Also, by the way, here you can also already saw maybe very fast, it's not just building a workflow, it's actually kind of already kind of tested it for you as well.

33:53So they actually can be sure that when you build something, it kind of, it is already in a working state. Really cool. And the other thing that I noticed, it's becoming challenging for especially larger organizations, is the deployment into products. This type of demos work very well when you have full control over the tools you're trying to connect. But as more people join the party, then you also have more controls and checks. So we're talking about large enterprises that already use NAN. How were you able to get over all of those procurement and guardrails problems to make sure that they trust your solution?

34:30First, we talked about before, self-assibility. I think that's just an important piece already that people actually know where it actually is running. And they can also very often use their own local models, for example, to actually deploy NN. And then the other piece is literally, if you build something with AI, 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 shouldn't send. But again, because we make it very simple, like literally this agent can do only those things. It cannot go rogue. We have exactly specified all the things it can be doing.

35:06Again, 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 they can easily understand and go through and kind of know exactly what it's going to be doing. I think that just makes it very easily for these enterprise organizations to not just again have a nice demo but actually kind of release it in production much faster with other systems. I think this is a very common challenge for a lot of companies that maybe started more on an SME segment or direct-to-consumer people who are like really curious and are building something and that they're bringing it to their enterprises.

35:45so 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 interestingly it's not just bottom-up we got 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 ever saw content around NNN and was wondering 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 NNN just in a more limited way and then it makes it much easier to actually do that.

36:28At the same time obviously like at some point you want to accelerate things. Another nice way to accelerate things is definitely partners and I think that is one thing that 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 it and 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 help you to kind of even figure out what can can be automated they're kind of this this accelerator we also work with um for example just a month ago we announced the partnership with accenture as well we talked with other system integrators as well the other partnerships getting announced very soon on top there.

37:08So they'll be looking for this kind of accelerators that kind of really help us to kind of reach more of our user base and reach more of the opportunity easier because we can obviously not have a one-to-one relationship with everybody. So 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. It's like you do the initial sprints with a client.

37:39They 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 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 automations? like what is your your vision on that we definitely see all of it theoretically like all of it is out there um but what like the thing we're doing internally as well which we um suggest for our partners as well is not just not having it only centralized like i think like 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 department literally has to kind of maintain hundreds of thousands of automation it doesn't work it's also the opposite of why i created nnn like the reason why i created nnn 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 build agents and and the idea with nnn was that the people that have the problems can solve it themselves because again that's why we try to make it simpler and simpler and simpler so like this again we have an ai and automation department internally as well but what they're supposed to be doing is like still help like the ideas 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 say hey i can 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 the kind of time to kind of the ideas to 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 starting with something small and kind of empowering them more and more that's how we really see um how we can really build it up successfully and then obviously document what they did kind of ensure you have the kind of the right the right tools in place but we kind of see them as kind of creating the guardrails kind of the day their own n at n their own everything around 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.

39:58They actually know the whole process by heart. And explaining it to somebody else is 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 connects and it's just super inefficient. Yeah, exactly. And that's why I think it's so complicated to measure ROI. 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?

40:33Right? 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 implementations. And here's 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 there's definitely also something we struggled with as well. Because there's so many, like I said, so many different ways to actually measure the impact. And again, we also started initially thought, hey, that's the simplest thing, but you can also do very easily. And it's literally just track how much time it actually saved.

41:06But I think that's actually not the best measure. We kind of realized it's also very easily. You can just cheat very easily in certain automations. So it's like even though they run every minute, it's not just, and it would take you 10 minutes to run it every time manually, it doesn't mean like it saves you crazy amounts of time there. So like what we actually now looking, I think one thing is the ROI piece, but what we are kind of, we actually internally, we care less about the ROI kind of measuring it the right way because we just know there is like, 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 certain things they want to achieve every quarter and then we as a company ensure that each and that each department kind of reaches those goals and we as a whole company actually reach um at least 90 percent 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 the impact i think you just have to kind of i think you still want to make sure like i think it becomes then problematic when you have like on one side an unknown outcome and on the other side you have a lot of a high It's very high spend because then it's not very clear anymore.

42:42And I think that's, again, why people appreciate NNN very much is because the cost of running an AI system built with NNN is much lower than in other systems because we're not just saying, hey, AI is a 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 figured out, they're not very reliable and they're very slow.

43:15Like why would you do it with an AI? And then you still have to have the human loop again, which again ensures that you cannot just use it for a nice demo, but you actually can use it for a business-centric use cases because you know the AI cannot do anything it's not supposed to be doing. And I think that is again why the cost of running an AI system is actually inbuilt and and it's actually quite low. And then knowing that the ROI is very clear, again, in the worst simple form, start with just start to track time saved, and then you can obviously go further and kind of literally kind of track your own things.

43:45And you can even do it dynamically. You can just say, hey, I have this arbitrary metric. 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 the kind of the impact what actually your 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 think this ROI thinking is one thing. It's great. You want to have ROI, but very often the advance is not just money saved or anything like that.

44:18Very often it's a better customer experience as well. Again, people prefer the AI assistant. Why? Because it never gets tired. It can't speak any language. It works 24-7. It has the perfect context. That's great. And I think that is this other thing. It's not just normally one value. It's certain things you can drive forward. You can say, hey, maybe you don't want to. It's not about saving money. It's about having better customer experience. Or it's about saving money. I don't know. Whatever your company is driving forward, you should measure that one thing. I like that because sometimes with the introduction of 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.

44:56While in reality, it's all intertangled. That is 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 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 the 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.

45:33But 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 is 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 the difference, if I have a human that does it and it takes him, for example, 15 seconds, I can do within 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 and what is important for you.

46:03Jan, it's been a pleasure to have you on the podcast and see you demo, see you also explain the product, go-to-market strategy. Thank you so much for your time. Thank you for having me. That was great.