 Hello everybody, good morning, good afternoon, good evening and welcome to Neo4j Live. Today, transforming engineering constructions and architecture with GraphRack and overall a nice conversation about AI, GenAI and graphs in general, I think, with my dear friend and former colleague Stefan Wendin. Hi Stefan, how's it going? Yeah, amazing. Nice seeing you former colleague, Stefan Wendin. Hi, Stefan. How's it going? Yeah, amazing. Nice seeing you, man. Such a long time. But I feel like yesterday, when I heard your voice, it's like, boom, it just took me back. Super nice. Super nice. So many good memories. Exactly. Good memories, good times. So, yeah, it's great to have you here today, Stefan, and to talk a little bit about Graph and also about AI and all things that happened in the past a little bit. today so um more more talking and less uh hands-on or or or slides but nevertheless i'd like to keep it as interactive as possible so if you have any any watches live in chat right now keep keep us keep us uh focused so if you see something i want to know more a little bit more have i have an own idea have something to comment on then please just type type it away and I'll read out your comments or questions at any time. So we can make this a virtual conversation between you and I and obviously with everybody else on the internet. So that would be great to expand the floor a little bit. Yeah, no, sounds good. So I always love to derail whatever thought might arise in our head and our plans. It's always good to just throw in a curveball here and there. After all, that's the real graph, right? The other one is just a set of tables and all the documents with plans. Nevertheless. Exactly. nevertheless exactly so um maybe before before we go into into the topic we have maybe maybe stefan do you want to want to share a few words who you are what you do and and the usual question is probably a little bit not as not as necessary i guess but you know what brings you to graph is is maybe uh maybe a little longer story i don't know if we have time for that today, but maybe share a few words still about you and what you currently do. Yeah, yeah. So I came from NEO, as you know, but maybe not everybody knows. So I run the innovation lab among many things. After a couple of years, five, six years, I moved on. And nowadays, what I tend to do is, where the main concern was to figuring out the space with graphs, which I love, but also in combination with other AI things, such as language models and so. So I am now doing two big assignments on 50-60%. You can also already know that I'm bad at math or have more hours than most people, which I do have. But one part is that I work as head of AI at Turians, an old school engineering company. So not the software engineering that the world we live in, the one that builds houses, architecture, structural engineering, a super nice company with the goal of creating better societies. And what could be better than super many hard mathematical problems that needs data with awesome people, right? So I run that. In that we're also building a GraphRag function, which we can return to. And then on the other one, which I haven't actually told anyone yet. So this is the first. You do first. Yeah, exactly. Here it is. I have the email address already set up. That's how far fresh this is. So I also work at AI Sweden, which is Swedish National Center for Applied Artificial Intelligence. Oh, very cool. Congratulations. Yeah, thank you. Helping bridge in like the science part with the applied part. And I think that's what I do the best. I read a bunch of papers, try to figure them out, ask even more stupid questions, and then I go prototype. And then I try a bunch of papers, try to figure them out, ask even more stupid questions, and then I go prototype, and then I try to kind of simplify that into something that we can repeat. So, yeah, that's a little bit what I do. Let me know. You know me. If you ask too much, I'm going to keep talking about myself now for the remaining of the time, so maybe just shut up. Yeah, no, that's good.'s that's great and that's that's cool uh cool too so you have these two these two you know jobs basically to to look at this and and you see basically firsthand so that's that's i guess where we where we started the conversation a little bit i i saw you post a lot and you just said this yourself you read and then you summarize things and then you bring the information the the the the knowledge you get you gathered across uh to your teams and i i i read a couple of your blog posts um you you posted on linkedin very actively um towards when this all journey i topic basically kicked off towards towards the end of last year and throughout this year and um I think that's really great that you have this inside view, basically. You come from a graph database company. You know the ins and outs of Neo4j. You see now the application of the technology in an AI space. And you bring that on into technically open and interesting companies. So I think that's a pretty cool story and a pretty cool... Yeah, I remember. I tried to figure out when it was. It must have been around like, what can it be, like 2020 or something, I had a conversation with, I think, Alicia or Jose about this idea of transformers and how, or if they were, then graph neural networks and not. And a bunch of months later, then Shetana Joshi wrote a very interesting paper and an article about this saying that they could be explained by the same kind of thing. And I usually use that analogy. I remember talking about this with Peter Velocovit at the Neo4j office for a podcast that never happened. But nevertheless, we talked about it, how almost graph-neural networks and transformer structure together are like, you know, beyond boy and John McAroe. Now, you know that I'm a seventies kid growing up, but what they did, they pinned against each other as mortal, like enemies. They hated each other, which was not the case, right? They were friends. They just have very different ways of doing things. But what they did, they lifted tennis. different ways of doing things but what they did they lifted tennis so even if you were not interested in it it actually elevated it up so that you got more air time and this is equally how i see now the benefits of graphs and the transformer structure together because they complement each other and build something much larger than the individual pieces and help out with shortcomings on on both sides and i think that's that's where technology becomes awesome because literally this is just how people are right if i'm good at x i find a person that are good at x and then we're still just no X. But if I find a person that know Y, we can take X and Y and we can compute set or whatever we like to do. Right. So I think that's the beauty of these. But yeah, very, very cool. Yeah, absolutely. That's super cool. And maybe we can stay a little bit in the of months um to before we dive in what you're currently doing because i think that was that was super interesting you you summarized a bunch load of articles and i can i can um i can link one um in in chat as well that that i found was very interesting that started in um you posted at the end of october called the future of ai in business unifying knowledge graphs and large language models. I guess that was kind of like a little bit of the kickoff of your, of your posting journey, I would say. How, how, how much, I mean, I guess it didn't surprise you, but how much were you, were you surprised? Maybe if at all of, of how, how, how much it took off within yeah that weeks almost yeah that's also yeah because like i i post the reason why i post is for me like uh it comes from one of my old mentors right so it comes from there's one thing to understand it by reading but then when you have to rewrite it in your own words that's a whole other level of understanding it. And then to publish it and put it out there, that's the next level because somebody is going to go poke on you. You got this wrong. You're an idiot. You're X, Y, Z. It's like, okay. So then I started just posting for myself, kind of, you know, I'm not good at that kind of imposter syndrome kind of thing. But I think that one was, so in the beginning it was my normal i have a fairly okay network right it's the same usual people that love the same usual thing if i post about a game theory the strategic side of my network was gonna love it if i post about something else the other side of my network i love it but i think what is also interesting with this uh almost like uh ironically or ironically, or it's on such a meta level, because I said that it unified. And what I also saw was that the business side of my people and the engineering side of my network, they also unified in sharing this. So I think that post actually literally kind of double exploded because it's also by the one sharing and commenting and reaching out about it also made it actually true in that sense so they almost like validated the the thesis in in that sense so yeah it was it was super cool and uh and really i think because i posted a couple of shorter versions on the similar topic i think it wasn't anything super particular about this but i think it was the timing all of a sudden people just got it right they got the graph part they got the transformer or llm part and then it's like wow i can actually visually now see how these two come together and i think that's one of those kind of i remember me and alessandro we talked about when we run the innovation lab uh very often we worked with very non-technical people right so then we saw the the light go on we call it the graph epiphany when they finally got the kind of graph thinking part right right? And all of a sudden you could just see like, zoop, they got it. And I think that article was that specific time. And then of course, as with everything, of course, now the topic has become super huge. And I think the great part is now looking upon how do we even make the smarter ones, right? By using also those. But I think that was one of the tipping points, one of my favorite ones also that people really liked and also added me. So that was fun. Yeah. Yeah. And I think that's great. I think probably you're right in saying that this is about this time when then everybody, technical developers, engineers were looking into it for a longer time, but also then the more the business people, everybody wanted to do something with the technology. More or less everybody understood that this is a chatbot system. I think we are beyond this now. I mean, we know it can be much more, much more, much more, it can be achieved with using this technology, but I think that's where it all kicked off. So. Yeah. I'm thinking actually, like now when hearing us talking about, but I think that's where it all kicked off. I'm thinking actually, now when hearing us talking about it, I think what was needed for it to actually reach the business people were that they could actually play around. So it's almost like this kind of, a lot of people go, AI revolution. I'm like, it's a fucking, oh, sorry, I cursed. Can I curse in here? I got just excited. I'm sorry. Sorry on the behalf of Neo4j here. No, but what happened is like, you know, you put a chat window on that AI structure. All of a sudden, everybody were allowed. So it's a mass adoption game, right? It's a UX revolution, rather. And because of that, and how bad GPpt 3.5 were early on like it was amazing but it was also not good i think like accuracy was 48 at at max 48 which is every other which is a drunk uncle at midsummer it's just made up right but because of that they could also now really understand that it needed something else to kind of ground itself to actually do that so i think that's also what happening because theoretically they wouldn't have listened i'm pretty sure right it would be very easy a very too complicated conversation but all of a sudden they have real life experience about hallucinations about frustration of not no i didn't tell you to do that go there do you want to go over here no no no it was here it was here right now they had that and then they could actually now see it if i invest in this that can help me ground this and secure this from from these things and which is very cool so it also helped a lot in conversation on you know what type of rag system you would like to build and how to make it better and so on so before it's seemingly impossible to get people interested in this topic but now it's like literally they do understand it or they want to understand it at least and spend time but sorry for interrupting you man i just got all fired up about the topic because i think yeah it is equally important to understand like why we change and why these things happens because that's what i look on the larger scale because then this is where societies are being transformed by technology it's just not a bunch of people playing around with technology because technology is awesome, which is also super cool and fun. But that's not the life-changing experience that we now see. So super cool. Yeah. And then with the title of this session, maybe we divert a little bit more into that direction. We said transforming engineering, construction, and architecture with RAG. I mean, that is, I think, probably more connected to your current role at Torrance. How do you see that? And was this a longer process that already kicked off a while ago or was this something that like really likes speed lanes over the past couple of months when when the technology was like approachable usable and like okay yeah we we need to do this now and this makes so much more sense yeah no it's it's a longer project so i started out that i worked with the innovation team uh and coaching then them on my thoughts. I actually contacted them about another gig, which was outside of the AI thing. But then they asked me, like, can you help us to scope out? How would the AI program look like? And I've worked very closely with Robert, who run Next, which is the next kind of phase of Therians, right? We worked on that. And then we come to the conclusion, we started to see like more and more this space evolved into, you know, okay, we can now actually chat with documents, but we have this kind of problematic part that we cannot share data. I mean, they build infrastructure, which is highly classified, which you cannot put outside on your own service and so on. So there's a lot of security things here. So then very early on, I started to draft this idea of using the graph with my previous knowledge. This is long before the paper from Microsoft that came out, I think, was one of the first ones on the title-linked graph rack. So way before that, I started drawing out things i sculpted out so finding a base version which we are now having in place and then building the kind of graph rack as a complement to that so i had a couple of wild ideas and synced them actually with jesus for the one listening it's another Neo4j person. I think they already know him about all of the amazing series you have done together. So they should know him. If not, go checking out. So I sounded with him and it sounded like a good idea. And then we start pitching that. So that led, then we got financing for the project. I started to build out the team. So it has been a kind of longer more kind of academic application process in in the back end but since i think we started at the top of the year really building things which is fun because i mean you can have a perfect idea on paper, and then you start building, and it's like, oh, yeah, there was a little bit of difference here. And I think what is interesting with Therese is that they have so many departments. It's a large company, right? You have people building bridges, roads, infrastructure. They have environmental people calculating all these things. So there's a lot of interconnected things. And already there you see a graph, right? Because like building a new city, you have roads, you have subways, you have buses, you have waters, you have electricity, ventilation, heating. It's like a hyper level graph already there. So I think the tricky part is like how can we make an architecture which is fast enough and super accurate and can handle contextual information in that sense because very often if if you're building these things you refer to legal and regulations which are you know you have a code for if you're building a road this is how the road should look and then you refer to that code and that code then refers to how another code and so on and so on and so on so there's a lot of linking already on that level as well which is first of what i've like this is 100% a graph this is not just similar information it's hyper connected in its in its. So that's what we are building. So we run parallel to a team. One is building out the actual interface kind of thing, and with a classic kind of, we call it vanilla rag. The credit for this goes to one of the Klarna engineers that stated it. It's a little bit disturbing to use it, but it's also funny. So we use it. We use it with a very much more kind of smile on the face. But yeah. So it's fun, but it has been a longer journey. And I think what also is kind of helpful here, because I think this is also something I do see a lot. Maybe you see it also in your side. We're not super far apart. But I mean, at least here in the Nordics, we have this face of the death of the POC almost. It's like, you know, the board told the executives like enough of your POCs. You need to get something in production and start making money. So this is the year of all hands on deck, all dead POCs goes on the side. Let's just build something, get money and learn by doing. It's not about fiddling around in the basement anymore. It's about getting into production, making money, learning by doing so, which is the kind of cool part. Yeah, and then and you know yeah produce something develop something that makes sense and that is actually generating uh something interesting so um yeah that's that's cool in the beginning you said um security uh you know deterrence is is having all the data not available publicly. And there's also this discussion about using an LLM with your data makes it, you don't want to upload your whole knowledge you have or be accumulated to a public space, which you don't have no control over anymore, the way it goes and what the system does with it. How did you tackle that? Was that a big problem in the beginning to convince people to use that technology also um i mean it's still sensitive so uh for those and i mean uh if you take the large infrastructure and like uh these kind of thing like traffic work at the one doing all the roads and stuff i think we will never be allowed to even put it in the cloud, if I've been honest. Maybe I am exaggerating here. But for those, there will always have to be a special occasion. This would be if you would work with any agency, I guess, the same thing and so on. So for those, always kind of hard. And what we're trying to do is now rather have the mix. So we have the RAC site with some sort of security and so on. But what we're also looking into and I'm looking into is also this kind of on-device smaller model connected internally. So you never actually go outside of your own network, so to say. So you stay within the realm of your firewalls. So I think that's also how these also can be handled going forward. And I mean, this is, I guess, the reason for what people have been ranting about today. I mean, Apple going hand in hand with OpenAI and Elon Musk is ranting about, they're just going to take your data, which is obviously true that they're going to do it. And that's the reason why they're doing it. So, I mean, they have been doing it ever since, I guess. Exactly. But that raises a point, right? And something we also talked about very often, because if you think about security, is it about being secure enough? I cannot access systems, which is how we used to look upon things, right? I have no access to that system. I have access to that system. The problem with this, even putting in a graph, is that you know this for sure but I mean if I have the graph I'm now allowed to traverse this meaning that I can actually understand things that I should not be able to understand and that's why I love the fine grain security thing with the graph because then I can explain it I can make sure to control it on that level that I really need so a lot of that is the important part, sitting down and saying and showing, this is how this is done. And now I can actually show it to people, which is also one thing which I really like about the graph, because it lends itself naturally to talk about data in a way much better than, I mean, you can't really show up like, look at this vector here. It looks so safe. What do you think about it? It just looks like an abstraction. I don't know. We joked about this in another call today. Is there anyone that can read vectors? And we're like, there's this guy on LinkedIn. He's doing all the different architecture by hand. So he's doing computation of graph neural network, but he does it by hand, AI by hand or something it's called. It's super funny. I really recommend you to check it or don't check it because I did, then I ruined the weekend that I should have spent with my girlfriend. I'm sitting there trying to calculate the different stuff by hand. Super nerdy, but a very fun tip. Yeah, that's cool. I mean, I cannot even imagine somebody doing vectors or making sense out of a list of vectors. That is, you know... It doesn't make sense, right? But the graph does, and I think that's the beauty of this. And that you can talk about these highly secure things because it's very easy to say, ah, but you have access to the system. Now you have access to all the answers that can be produced. But this becomes this kind of narrative of implicit explicit, right? One, you have access to, but then you can start inferring,ring ah this together with this actually means this and you don't have access or right to access that part the the answer on it so it's almost on a node node-based security in that sense uh which is at least on a query level so that's one one one of the things i really like it also allows to sit down and talk about these things much more easily. But it was a very hard topic. And I mean, for a lot of these old companies, it's almost like, if you think about it, it's like, you know, it took a very long time for them to ease into digitalization. And maybe they haven't fully emerged already in that. And now we have a second wave coming. And this wave, it's much bigger, right? And the same thing goes. It's like, get your data in order, for God's sake. Don't bring it. I don't want to see a big tank ship with crappy data. Just give me a small set that actually works, that people love to use. Exactly. I think that's a good uh a good thing we always keep saying here and i think that's that's ever since it's neo4j i said okay yeah let's let's let's pick a pick a small small set of data you can oversee you understand what's going on here and then you you you transform that into a graph and see if if that if that makes it easier easier, makes it better for you. Yeah, totally. Are you in the position to share a couple of things? What the transforming or what the engineering, construction, architecture transformation, I guess, in your current role, what does it transform, actually? Yeah, I think we can talk about a couple of ones, some which is somewhat sensitive. But if you think of... We don't have to do those. No, let's not do those, then I get fired. Then I have to come back to Neo. You would regret this. At least someone would. But nevertheless, I think like the classical thing here, like we talked about this, is it's highly regulated what you can do and what you can build, right? So the first part is this extreme Wikipedia lookup, right? Every document connected to every other document in that structure. So meaning you can imagine doing that this is this is a full library so you're building something you have to go and looking on you know tens or a hundred different documents at the same time when writing this manually in by hand and then you repeat that for every single time which is hello it doesn't make any sense right and this is exactly what what will be one of those so building that kind of sidekick i usually make fun of this because we named it sven and it's because of the founder right but i always make this story because my middle name is sven so i make this weird narrative that that I'm a narcissistic idiot. Or maybe I am. Who knows? That's exactly what the narcissistic idiot would say, I think. But nevertheless, I think that first transformative part is that part. The interesting part here is that this is a space. I mean, they sell consultancy by the hour. So it's actually not beneficial to solve it faster so i think that the bigger harder figure here to figure out is they have to go from selling hours into selling value which is a whole other game so to go back this is almost like you know neo4j on-prem you know you sell the large thing you have the capacity you need it so you don't hit the roof but now you go to the cloud which is a completely other game it's much easier to deploy and take it down and so on so that's a whole other value game right so we need to be better at that so i think this is very similar to to that time and i think this is something we see very often repeating so I think that's one of the most obvious ones. Then we have something which is very similar to amazing Neo4j project, which is the KSAT stuff by David Misa. So we're looking upon talents and so on and trending talents, topics, skills, technologies, and these things and how they move over time. So this is the secondary one, which is not super sensitive. I cannot tell you what we're going to do with that, but you can imagine what we're going to do. Literally solving things much faster and much better and helping people to do good things with those. I think those are the very interesting ones. Then we're doing a lot which is very heavy on the gnn side which is predictions of things let's see how i can say this without putting too much information out so imagine you needed to compute something which is super hard like weather or vibrations and things like sound waves that do different things. So doing a lot of modeling on these things using GNNs as well. So that's also pretty neat. It's very little kind of of the LLM hype in that we only use the LLM for that case, actually as an interface. Right. So we have the tools in the background running the computation, but then we just use the language model as some sort of interface, which I think it also does a very, very interesting job in doing, which is very fun also coming from a UX perspective in looking at this. So you can imagine with that also you got the structural engineering. I'm building a house. It needs to hold X floors. It needs to hold X people. And I want it to look like this. How can I now build it in a most safest way that is the most environmental friendly that also looks the way I want it because i mean it's not easy to build something very secure it's just very expensive and it's also very bad for the like environment to add this you know steel concrete and these things that are already very bad on a large scale so if we can take that down we can actually make the world a lot more the way we want it. So I think that's also one of the really big ones. That's a lot of more computational kind of thing in that. But in that case, then we do a lot of the computation with GNNs and stuff. And then we just used our language model as the interface for that. So we have the overarching kind of model, then it has the router, then it goes down to different kind of rag structures with the graphs. And then we have the function and tool calling for the specific tools for these computations and stuff. So actually turning out pretty neat. But then again, I mean, the transformation part here is that we're literally redoing everything that they do in that business, which is going to be very hard for a lot of people, right? So which I think is very kind of interesting in that sense, which also led me to, that I built a couple of my own tools that I use to measure transformation in organizations that I build on Neo, actually, which is kind of fun. Funny, funny enough. Because very often, I mean, we think about change management and transformation as, you know, or a lot of people talk about it or treat it like this. Here's the old, here's the new. You go home on Friday and on Monday we have a new logo and it should be all easy. That's not how it works. We all know this. This is the biggest lie of whatever marketing or whoever branding come up with this idea. That's not how it works, right? I change because you change and i respect you so it's a line of people that influences each other so this is is the classical old way of law of diffusion of innovation is called is the one where the innovators the uh kind of you know yeah that a classical kind of early adapters majority late majority, late majority, laggards and so on. So I built that and there's this great actually YouTube movie that was my inspiration. It's called Leadership Lessons from a Dancing Guy. If you have not seen it, it's like this is everything you need to know about leadership in a very condensed format. So everybody go check that one out. But I think then what I built is literally looking upon this kind of personas within the company so you would have the change makers right you would have maybe a good storyteller somebody that can talk about your very technical product in a way that people without domain knowledge could understand you have the well of knowledge, the person that knows every single thing in the company. You know exactly who I'm talking about now in Neo when I say that name, right? And so on. So we have these personas, right? So what we did, we did a set of town halls, for example, for finding these. So we had open questions. We then narrowed down the behavior types and frustrations and struggles and answers down to these kind of different personas. Then we let people nominate on this. And then we just pull this into a graph. Then we run PageRank on change makers to change makers. So somebody nominated another change maker, right? And then we did a version of article rank and clustering with k-means where we can now start seeing this behavior clusters of things we can now add the other people the traditional hierarchical thing because now we have a graph with everybody that are showing some sort of behavior that we would like and then we add all the people around it, which does not yet show so. But instead of going to those people and saying it, because I don't have any credibility with those, I can identify who sits one degree out and how can I help that person to help that person that can help that person that can help that person. And all of a sudden, we have now a visible way of seeing change in the organization by modeling the organization as a graph rather than the hierarchical tree that we pretend. It's not like I ever went to my boss like, I really want to know how to build this. I better go talk to my boss now. This is not how we do things in Sweden. Definitely not. This is the last person I want to go to. definitely not this is the last person i want to go don't do this don't do that or she right so i think that's also a very cool use case that we actually did on on kind of understanding uh inofficial leaders within organizations because i think that's also something that it's so cool all of a sudden this is super easy it's super simple you see it and you spin this up in i mean it's a matter of hours of a sudden, this is super easy, super simple. You see it, and you just spin this up, and I mean, it's a matter of hours. I mean, the first prototype I did was on the airport bus in South Korea, so from Incheon to Shennan, which is two and a half hours. In that time, I imported all data, spin out the graph, run all the algorithms, and I had my first draft coming from. It's like, how can this even be? It's so easy to just try it. So I think that's, which is also the reason why I ended up in you in the first place, is that easiness of putting it in, seeing doesn't make any sense. It does. And then putting it back out there. So yeah, now I ran that for a very long time. All the questions we're supposed to talk about, but sorry, man. Yeah, no, that's all right. But I think that's a great point you made there i think in in especially with with this transitional period we are in now with new new technology new new ways of working new ways of you know dealing with data or tackling data or or overcoming data challenges you have and and then this is it tends to become quickly us against them kind of like old traditional people to to new technology or something and these are all it it's never really meant to be that way it never has to be like this hard hardcore confrontation it is it is obviously there is there is some some of that around but you you know people can be convinced i think you just talk to them in the right way and if you find the right level and the right approach if you say hey this is for tomorrow we're doing it this way and you don't have to have any say in it well that's going to be a tough sell but this goes back to that book you know Connected by James Fowler right so all of the data pointing towards me says less than the data from my network so if i can understand who is around me and how they will behave that will tell more about any of the data points about me which is exactly this right so by identifying the the people one uh i don't think in that book let's see now i think it's 1.7 degrees out or something so almost two degrees out or two persons out two hops out uh i think that's uh where the biggest impact is and i think i mean if we can already see this we can actually help people helping other people and i think this is this is how normal this is how society has changed we haven't changed in that kind of binary from here this this doesn't work like that that's friction right there so and i and i don't want to be that person so it's a rather interesting way of applying that knowledge uh the irony is then i'm i'm here building a system to build a knowledge system, but then building another system using the graph to understand how does the knowledge system impact the people graph, which is a very cool kind of double kind of use case. But I think, which I 100% going to give credit to Neo. I would never, ever thought about that idea if it wasn't for my time at Neo, because all of a sudden it's like, 100%, this is a connected problem. It has nothing to do that they're in department X and department X as X percent, which is true. Engineering, they're more like this. And, you know, architects, they were more like that. And it's like, yes, but if I can see it on an individual level, I can actually make sure to help the people which is exactly what we want and which is a core human need i mean we want to be here heard we want to be seen and and if that happens we we should be good yeah yeah absolutely that's great and uh i think with with that, it makes a lot of sense to look into that and to try it out and to discuss and find a new way of working into hoping that can then be easier. Do you have anything else you wanted to share about this transformation process in this current work you're doing with Viterians? I think, I don't know, whatever it could be. I mean, we are, for next year, looking for talented people. So I do a shout out for that. If you love AI and graphs, hit me up on LinkedIn. Let's see. AI and graphs, hit me up on LinkedIn. Let's see, maybe we come back here in six months and tell Alexander about the awesome things we have built and do a demo of it. So that's how I think about the transformation. I think that's pretty much it. And I think that it all comes down to this classical kind of thinking. And I think one way that at least how i look upon these things is this idea of you know we often look upon the bigger transformation if we say so uh it's like oh now we have another technology now we need to transform but the important part is i would argue how does that new way of technology and rewrites us so that's one one way and i usually use this analogy of different paradigms, right? So first paradigm would be when we would start writing things down. You know, what's the benefit of this? All of a sudden I know, Alex, you owe me four beers. I can go back and I can claim it, right? And I mean, I usually use this old example of the Uruk society. They have some beer tablet, right? The only problem is it's on clay and it's hieroglyphs. So it's good to store a clay tablet next to beer. No, it's not very good because water and clay, they're not best friends, right? But this is equally true today, right? For the value to be true, you need to write it in a language you understand. You need to store it in a way so it's accessible over time. And I mean, look at COBOL if you're in fintech, whatever. You can't even find your grandpa's developer almost. It's like such an old technology in that sense. So a lot of the cool fintech, they're already backed on this old clay tablet technology with the syntax that looks like i i don't want don't even want to say it but i think that's the first paradigm then we come to the second one right which is the printing press this one would be then you know all of a sudden you can distribute things very easily to a lot of people right so we can take uh luther and the gutenberg bible classic example so for this to be true we have to understand what the previous one did they could write it down in fancy latin on parchment roll they ride on a horse then distributed that to every city took forever right but it was true it took forever what did he do he wrote on plain folksy german that everyone could understand and he made small printed pamphlets that he put on every single door all of a sudden the conversion it's like boom so you can overlay a heat map of printing press versus that type of Christianity in Germany and they 100% align which is super fun. What we have learned from this is then if we are to communicating to one to many meaning unknown people we need to adapt the message accordingly right so in that sense it all makes sense we need to make it available and then readable for the many so we cannot have a complicated Latin system we need to have a language which we can understand. So a simplification of language and data structures equal that. And then we come to the next one, which is of course the kind of graph paradigm, right? So I think that's how I look for those kind of things. Because all of a sudden, when you start looking at this you start understanding that you're not going to have any success with your language model chatbot whatever project if you don't get your basic right do you have readable clean accessible understandable you have relationship you have your feature engineer you have a process to support all of this if not go fix that So you could say that the value of your AI initiative is directly proportional to the quality of your data or your data quality process. But very often, this we don't talk about. We just want to have the latest, latest. Oh, it's boring. Yeah, because the other one is work, right? Yeah. And the other one is work right yeah and the other one is working yes yeah so i usually use this when i talk to boards because everybody's like oh this is fine let's do it so then i i talk through this when i when i meet new companies and i always use this analogy of this i don't know what the the meme is called but it's this uh dog in the house with this house which has the fire around it. And then he says, this is fine. This is the only board that I ever met. It's like, this is fine. It's all there somewhere. I'm like, no, it's not. Because I think this is the real problem. Because then what you need to do is then say, should we clean this? Should we re-engineer our process? Or should we just draw a Should we re-engineer our process? Or should we just draw a line and start building the new? I have no answer on this. And this is a tough call because if change would be easy, that would be leave on Friday, come on Monday, and it's all perfect. There is no perfect answer. And everybody that says so,'re lying but i think thinking more about data quality and finding whatever from every single paradigm in there because without those you're not going to have any value with any of your ai project ever it's just as simple as that that's a good good shout out cool um maybe we spent a couple of minutes on questions. A couple came in from the audience. So I'd like to read out a few to you and then we can obviously, you can take a stab at them. I'd like to start with Nani Sinek. They ask, do you have suggestions what to use to model a graph schema visually and use it as input for your ETL pipelines to create graph and populate your graph with data. I don't know if this goes a little bit too much into data. I started reading another question which I thought it was so I didn't listen. Can we do the question again? I was trying to find it. I heard the name and then I started reading that, but I think he has been doing a lot of them. Yeah, here it is. A little bit further up. Suggestions on what to use to model graph schema visually Yeah, here it is. A little bit further up. Suggestions on what to use to model graph schema visually. And then, I guess to use input tool for your ETL pipelines is maybe a little bit. I can talk a little bit about that. But do you work with any visualization tools? Is that anything you... No, I mean, i was brought up the hard way with the whiteboard uh that that's how i learned doing this and then i just converted but i mean looking on the stuff that are available now in the aura suite you know where you just draw out your graph and then load your stuff in there i think that's one very easy way to do it there's also a very interesting talk i think on what is it now two days on first day uh with with thomas and lama index so there's a lot of these kind of neat ways to auto generate the graph so that's a lot of what we are exploring and i think lot of what we are exploring and i think the best recommendation is for me to to find try a bunch of stuff see which one works for your team and your workflow and then don't try to find a silver bullet i haven't yet not seen any super silver bullet that handle everything and there's so much happening in this space so i would just go for whatever get the job done and make sense for you and your team to talk about these things but i use 90 percent the stuff in aura but i tend to do a lot on my desktop and browser still yeah the hard part is i think it's it's what you learned and then that's what you're going to be doing yeah and so yeah no but they are fairly similar you can you can use them interchangeably i think there's also arrows um which if you just want to if yeah if you want to just draw out the graph and then play around with it without actually loading the data in the first place but you just want to see visually how does it look like and again to what we talked a little bit about earlier when you start with a smaller data set you get you can you can model that out easily and then draw it on a on a even on a whiteboard uh or use use that that arrows and and get started from there and then you know like like you know stefan said take take take what feels natural to you take take what works and and what you what you feel gives you a good good uh good impression and then take what works and and what you what you feel gives you a good good uh good impression and then elaborate on that and and develop um on on that um knowledge basically yeah no totally the another question comes from eric um basically confirmation of if you heard the use case for for graph right correctly. I mean, the ones we're talking about now, I think there's a lot more beyond this session, but I mean, a couple. And then he continues, one is Sidekick for crawling a wiki-like data store. The other is an HR network of behaviors, question mark. And three, creating an interface for tools that model buildings. Yeah, something like that. And I mean, the whole thing is like, the way we look upon it is, or I look upon it is like, I see a lot of the language model as a replacement of an interface for doing other things, right? So that's kind of one thing on the front. And then we're using the graph and the knowledge graph, per se, to handle the very highly connected data within those regulations in the back, right? So we want to be able to traverse this, you know, sometimes even looking on. And that's what we're experimenting a lot. How can we create the feature engineering on those? So I think that's one part. And then using to call, function calling or tool calling to call and do computation elsewhere, and then pull it back in again to the user interface. So a simple user, flexible user interface, because we don't need to have the kind of airplane cockpit, right? And then we actually have the airplane cockpit behind. It's just like, pick, put in whatever you want, outcomes, everything. So we handle all shit in the back end, just as front end should be. And it's like this old saying, I guess, the less design you see, the more design it is. And this is equally true now in this phase, I think. It looks very simple in the front, but what goes on is the combination of them in the back, combining different type of rag, different type of retrieval systems with different type of tools and then created this kind of agentic workflows. You get the perfect answer coming out to you as an individual with references and all parts. But at the end, I mean, that's a bit. So I think it's a good part. And what is interesting is that all of these use cases kind of intertwine in each other. And what they do is then you start to build this knowledge infrastructure because that's what I'm building, right? It's not that I'm building a system here. It's a knowledge infrastructure because then you can use it to different things. You don't have to rebuild it. It's rather the brain that then can be solving all of these other things. So that's how I see it. So I don't look so much on a vertical kind of use case kind of thing. I know that this is how a lot of people start doing it, but I'm trying to kind of use case kind of thing i know that this is how a lot of people start doing it but i'm trying to kind of think about the bigger kind of underlying structure and then take it from there because i know there will be connected use cases everywhere anyhow yeah um another question from alejandro maybe i think you you talked a little bit about this using different rack systems and um did you when when you were driving this did you did you compare different maybe graph rock versus something else or tradition traditional rack is not even is that the thing now i don't know you know we call it vanilla rack no i'm sorry it's super funny to use though. No, but what we did is, I mean, just doing a regular rack, this is like, or naive rack, that's out of the question very often. So the one we're running now is a combination. So for the simple system, we're running like a vector store. Then we're using hide. What is that? Hypothetical document embedding. Yeah. And then we use LLM re-rank. So we actually come up with similar, just like saying to the model, like, okay, imagine that you can answer this question, even if it doesn't know, then take that answer, embed that answer in, retrieve all the information that we now get back because the problem often is that your question are necessarily not similar to the answer so this now it makes up that so we have more retrieval power coming back and then we re-rank that again and therefore we have a much more accuracy so that's our simple version so we did experiment a lot my colleagues also wrote a paper on this which is called aragog the first one is out we can maybe share that in the comments later on on linkedin or something we are now writing the second paper which is on advanced generation of graphs and then retrieval of information from them. So that's the second paper. So I harass everyone that has been doing any work. I pinged the Klarna people, I pinged some old colleagues in Neo, but we are in the first part of that. So we now doing, because the tricky part here is also figuring out the cost layer, right? It's not super hard to do something that is super efficient. If this should be allowed for 10,000 people to use every single minute, it cannot be very expensive. That's going to be, then you get fired quickly. So it's about figuring out, you know, cost versus efficiency balance here. And for which use case needs what. Of course price will drop, but for the meanwhile it's about experiment. But I'm happy to share the papers and how we come to the conclusion. As I said, we use the VectorStore with Hyde and LLM Rerank. That's the one we did. Seems to be working for us very much. But happy to share anything about those if anyone is interested. Yeah, send me those links afterwards and I put them in the YouTube and LinkedIn video description and then you can check it out. All right, maybe one more from Katie. She asks, do you believe knowledge graphs as a business's digital twin will disrupt the traditional monolithic functional landscape will traditional systems like erps have a place in our organization's future landscape i mean uh so will traditional system have any place i mean a lot of this is changing. And if we look upon it the other way, rather, so I usually use this other analogy of this, right? Because as long as we get the data to work, then it works, right? That's the value. So the more we can get it to work, the better it is. So that's why if we have something that always changing all over and all over, is it better to wait and build a perfect structure for something that keeps moving? When do you start building that perfect structure? It will not happen, right? So the problem is that it's always moving. Therefore, I think you need to put your eggs in the basket with more flexibility. That's at least how I do, because then the thing is here that we need to equally rebuild the organization. It is not to build a perfect system. The perfect system without users has zero value. So it's, which is also how I think about this modern type of AI system, right? So I usually say like, imagine having an office. office okay we buy an office it's full of people none of them doing any work does this equal value no that's very expensive shit the same goes for data you put lots of data in a perfect system that is very beautiful if it's not being used it has no value it's a potential value that can be realized. So people and data at work is where you have the value. However, people in synergy also have a greater value and equally so does data. And that is then the argument for the knowledge graph system is then the connectedness of things, right? The ability to connect, the ability to create this agentic workflows and architectures and the fun part here is that they start to look more and more as to how we treat humans and teams in that abstract world so i don't know will it replace it or not i i the only sane answer is we will live and see uh i mean but i will put my bucket in a system that allows me to flexibly, flexibly adapt over time because it's equally important to upskill the team. So they don't have a freaking heart attack when it's time to change. So it's rather of nudging, right? That's how we change this. We try a little with, with somebody I like, how did you love to start loving cilantro why did it all of a sudden stop tasting like soap because you tried with a bunch of friends when you went to New York and you had the tacos at that Mexican restaurant it was pretty cool and you were also a little bit drunk and that it helped out and now you love it right that's how it goes so that's how i think about it i have no right answer on that one but no it's it's it's it's tricky obviously to say but i i mean i think traditional systems had a place for for for a reason uh in the past decades uh as as a pillar of of of of of many many architectures that have many many big companies and enterprises use that so i'm still using it so i think i don't i don't necessarily think there will be it will be a complete disruption and a complete replacement or rot everything out and put put the new stuff in but i think it has to be like you said flexible has to be adopting it has to it has to make way for for new technology and knowledge graphs and digital twins can be exactly that. And if traditional systems can be the baseline but you already have and then you add to it and you make it more flexible and enriched it, you make it more approachable, you open it up basically these sometimes a little bit old, a little bit rigid systems, you open it up to this new technology, new ways of working with the data. I think that's what you all want to achieve. Let's look back on the example I did. We still write things down. It's equally important to write it down in a language we understand. Otherwise, it has no value. The sharing value goes away. So all of these paradigms goes hand in hand. This is that this idea of it's a binary change is a false dichotomy. There will always be an infused version of these. So parts of it will be there. But for the connected part, I mean, it's obvious that it's a graph. I mean, you see it everywhere. And you wouldn't be in this call if you didn't believe it. So let's go for it. Should we do another one or is it timeout? We are at the top of the hour. How fast are we? We are at the top of the hour. It does go quick. Maybe we do one more quickly from Nani Sinek again. Do we have suggestions on which LLM transformer to use for reliable cipher generation? I experienced a lot of mistakes with LLMs writing the right cipher query. Did you ever try that? Oh, yeah, we're doing a lot on this. I think the tricky part with this is to put it in production. I mean, there's one thing to have a sidekick that I built my own GPT, for example, that I run helping me when I write things. I forget to just fact check. I mean, you could do a lot with adding critic layers and so on to get them to be more accurate. I think that's good. The tricky part with the generation part is that this model spaces moves so fast. So when I put something in production and open AI, for example, then go change the model. It doesn't work all of a sudden. For example, Thomas had this example when he built something that worked perfectly fine, and I'm like, it doesn't work. It's broken. It works. And then he checks, it's broken because somebody changed the model in the backend. So now it cannot generate again. So I think that's a tricky part. So I have no real good answer. So on an individual level, just build whatever you do and then use that individual knowledge rather to scale that up. For general things, generating graphs and stuff, you can do it. I mean, the tricky part here again, it's not to find a very expensive flow that can do a good job. The very, very hard part here is, can you make it so that you can use, for example, 3.5 if you're using the OpenAI suite and still get accuracy? Because at the end, it's going to be a cost thing. But what is actually funny on this, I did one of my favorite teaching for Hyper Island. So I usually complain that I'm not doing any technology parts or very little of actual coding. So this year, I decided, or last year, I decided to build a GNN using Neo4j and do a recommendation engine on Spotify, which was fun. I had 50 students, I have four days, and I have two persons that know how to write SQL. You can imagine I was sweating. I'm like, it should technically work out. So I prepared a fake data set that I also messed up. So they had to clean it. They had to normalize it and do all sorts of things. But then they could work with this together. And the fun part, that time, it was trained on an old version of Neo. They were building on a newer version. So they had to go from the documentation, get the syntax, put it in. This is a new syntax. So they had to do the memory prompting, right? So I think instead of just going A to B, find a workflow that works, right? Instead of saying, can I have a model that works? No. Find a workflow that actually works. So maybe you have you add the documentation you add these type of things you see that it's do a lot of errors you go in and specifically uh correct those so that's how i do it and and then you also learn i noticed very quickly what has changed between the versions and why does it do it oh because that's how we did it before which was equally one of my problem coming from neo learning being a little bit too curious poking on everything in the alpha tier oh how does it work how does it work having no idea how to use it honestly and then you know i'm trying to use that workflow now and obviously that's changed since long gone and i keep there's a specific error that I do every single time. And people are like, shouldn't you know this? You did this error already a year ago. I'm like, it's hard coded in my database. I can't get it out. So I think just figure out the workflow rather than finding a specific model for the thing. And then see what you can learn from that. Instead, I think that's much more interesting. But it's a good question. And we're going to see much more on that, I think. Super. Cool. I think we are over time already. So I think we have to wrap it up now. If people want to get in touch with you, then your LinkedIn link is in the video description on YouTube and in LinkedIn as well. So I guess reach out to Stefan for more discussions and more insights. Thank you very much for spending the time with us today, everybody. Thank you for, for, for watching and listening in today. Thank you for your questions. Sorry, we couldn't answer all the questions today. And obviously thank you very much, Stefan, for, for spending the time and, and, and the, oh yeah, cool. This is the link. So I'm, I'm going to add the link into the chat right now for uh the paper that stefan mentioned and i'll add it to the to the other places so um yeah thank you very much stefan um thank you guys if you want to um watch watch more then tomorrow actually we're gonna do our next session and we'll have a life science workshop that's happening tomorrow afternoon Central European time, morning time North America and evening time Asia Pacific. We have a couple of interesting guests from University of Heidelberg, John Snow Labs, KIA Gen and other, AstraZeneca for example. So check that out. That's going to take place tomorrow. Obviously, Nodes is coming up. So the COFO paper closes this weekend. So if you are interested in submitting a session, then please do that. And, you know, we have only a couple more days left, so do it now. There's a lot to fill in. It's very hard. We will submit the GraphRag paper we're writing, so it will be coming, but it's going to be on the very last minute. Yeah, that's fine. Yeah. Cool. That's great. I look forward to your submissions and when everybody else is invited and encouraged to submit. If you want to know more, what's up, what's next, how to proceed, the Graph community is very friendly on the forum and on Discord. Graph Academy is the place for you to learn and start with fundamentals as well as an LLM fundamentals training and other how to work with vectors to stay with the topic on today, but also more cipher data modeling training. So they're all free online. can take that um anytime you want and if you want to play with the product aura free is the place to go um if you just scan this qr code i guess then you can get to our developer landing page and from there you find everything else and yeah with that uh we are we're done for today thank you very much uh everybody for
Neo4j Live: Transforming Engineering, Construction and Architecture with GraphRAG
Discover how to develop a comprehensive knowledge structure using a multi-expert/agent system equipped with advanced tools. Learn about taking an innovative approach, organized within Retrieval-Augmented Generation (RAG) structures, and see how GraphRAG is revolutionizing the fields of engineering, construction, and architecture. 

Guest: Stefan Wendin, Tyrens
https://www.linkedin.com/in/stefanwendin/

AI Sweden: https://www.ai.se/en
AI in Business: https://www.linkedin.com/pulse/future-ai-business-unifying-knowledge-graphs-large-language-wendin-fof3f/
ARAGOG: Advanced RAG Output Grading: https://arxiv.org/abs/2404.01037

0:00 Intro
8:42 GraphRAG in Engineering and Construction
14:42 Detailed Use Cases of GraphRAG 
27:46 Transforming Business with Graphs and AI 
45:22 Q&A
1:04:44 WrapUp

#neo4j #graphdatabase #rag #graphrag #construction #architecture #engineering