---
title: "Interviewing Séverine Nolf: building the AI workforce for finance & strategy — Bright Signal"
url: "https://brightsignal.fm/episodes/interviewing-severine-nolf-building-the-ai-workforce-for-finance-strategy/"
description: "Séverine Nolf: Building the AI workforce for finance & strategy. A founder interview on Bright Signal, the European tech podcast."
---

EPISODE 038 Founder 13 JUL 2026 54:30

# Interviewing Séverine Nolf: building the AI workforce for finance & strategy

[Download MP3](https://media.transistor.fm/5c45e198/58a5acea.mp3)

![Episode 38 artwork](https://img.transistorcdn.com/m3mTVdpUooawF5n2OLFMaB-4eQPvWnSOg1oYH72bou4/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS80ZTZl/ODI1NTE4YWQ0MjY5/NGU3Yjk1Mzc5NmFl/YzliNC5wbmc.jpg)

ORIGINAL EPISODE COVER · E38

EPISODE **38** DATE **13 JUL 2026** DURATION **54:30**

[Read transcript](https://share.transistor.fm/s/5c45e198/transcription) [View chapters](https://share.transistor.fm/s/5c45e198/chapters.json)

EPISODE NOTES

## Show notes

In this episode, Bart and Raphaël sit down with Séverine Nolf, co-founder and CEO of PleaseFix.ai, the Belgian-American startup building the AI workforce for finance and strategy professionals. Séverine and co-founder Maxime Lahy left McKinsey's internal AI team to bring agents into the tools where analysts actually live: Excel, PowerPoint and Word. We dig into her three-layer vision, from last-mile document agents to a collaborative operating system, all the way to firms' agents negotiating NDAs with each other, and why you have to ace the first two layers before anyone lets you build the network.

We also cover starting the company in San Francisco while keeping the tech team in Brussels, landing meetings with the biggest banks six months in, the $2.1M pre-seed led by Pitchdrive, why PleaseFix wants to be "the Legora of finance," and how the analyst job shifts from crunching slides at midnight to building conviction.

🙋‍♀️ Guest: Séverine Nolf — co-founder & CEO, PleaseFix.ai  
📩 Connect on LinkedIn: [https://www.linkedin.com/in/severine-nolf/](https://www.linkedin.com/in/severine-nolf/)  
🌐 Book a demo: [https://pleasefix.ai](https://pleasefix.ai/)

## Episode transcript

Read the full conversation

Transcript from [Transistor](https://share.transistor.fm/s/5c45e198/transcription.txt) .

Raphael: Hi, everybody. Welcome to the Bright Signal podcast. My name

Bart: is Bart. I'm here with my friend, Raphaelle. And we just sat down with Smeets from pleasefix.ai. Yes. Smeets is the CEO and cofounder of placefix.ai, a young Belgian company, but also US based.

And they are building the AI workforce for finance and strategy. So let's go to an interview. Let's check it out. Hi, Smeets. Nice to have you on.

Thank you for joining. Can you start by introducing yourself and explaining a bit your background?

Severine: Yes, of course. Thanks for having me. So yeah, in short, right, I'm Smeets North. I'm one of the two co founders of pleasefix.ai, co founded it with Maxime Lais, now a bit more than one and a half years ago. But in short, on my own background, I studied business engineering in Belgium, and during my masters, I specialized in two kinds of fields, data science and finance.

So always had kind of loved those two fields, didn't really know how to combine them. I was still fresh out of uni, got an offer from McKinsey, a large strategic consulting player and thought this will be a track to continue learning a lot. Like I just thought that's probably the place where I learn a lot. So I just went there, had a lot of fun and spent a lot of time on very different topics. But towards the end of my three and a half years at McKinsey, I spent a lot of time on the AI team, developing internal tools for consultants.

And that's a bit how I, one, worked with my co founder Max, that's where we both were on the team, and two, where we got the idea and obsession for leaving McKinsey and going out there and building our own startup that is called Please Fix.

Bart: You mentioned the the internal tool at McKinsey. I think this is the one called Lily, if I'm not mistaken, which is the McKinsey internal platform deployed to more than 30,000 consultants. That's that's really impressive. It it feels a bit like you built the prototype of of Pleasefix within one of the largest consulting companies worldwide, where you're, like, staring at each other at this moment and saying to yourself, now that we've built a prototype, let's build the real thing and the real story outside.

Severine: Yeah, it's definitely a lot of that, right? We learned a lot by doing that. It's very different to build a product within a service firm for a lot of reasons. Like the tool you just mentioned is internal, right? It's not client facing.

And at some point, I think, yeah, it was the right combination of learning a lot on that field, how to build products and for that exact ICP and the right combination of becoming more and more obsessed by the feeling of like, actually, this is the best moment to just go build something and sell it to the full industry. A combination of learning, having done the job ourselves and having a clear view on who else out there could benefit of this. I think McKinsey is an exceptional place to, like they just invested a lot in AI, right? They clearly, like from the consulting players I know, they clearly wanted to lead that research and innovation side. It was fantastic to be on that team at that moment.

But then I also think, I also think the rest of the industry needs a player to buy it from, and then there is of course the limitation of McKinsey being structured as a service firm and another tech company. So, yeah, it was it was great to be on the team. And and and that's really what fueled, I would say, the the the start of of our company.

Raphael: Very interesting background. I'm also very curious who what please fix DOS. Like, what is the product? Like Yeah. What are you trying to fix?

Severine: Yeah. Yeah. So, of course, I've I've several versions of the pitch. Right? But in in very short, the the the one sentence is we are the AI workforce for finance and strategy professionals.

And that means a lot of costs, but the whole thinking is like, if you are ending up in those jobs today, you will spend a lot of your time on manual work and grant work, let's say. And we wanna, and you will spend only a small part of your job on really building conviction about the deal or about a certain project you're on. We wanna like completely change that. And in the end, the overall goal is we think we can create liquidity in the market. So that's a bit like big vision, but how we do it?

Because I don't know if you're also just asking about the products, right? Or more like in general, but I can share more on the product if that's-

Raphael: Yeah, maybe the product, think explaining the product makes it a bit concrete, like what type of users are you aiming for? What are you trying to automate or to support with?

Severine: Yeah. So in short, when I say AI workforce for finance strategy, we sell to four types of clients. First one is private equity. Second one is investment banks. Then M and A advisory at large, and like a bit larger than just IBs.

And then also strategic consulting. And the product is like, when we started the market, like, so we started in February, March 25, we started by saying, actually, if you look at where analysts associates spend most of their time, it's in Excel, PowerPoint and Word. So we started by saying, we're going to build the best agents integrated in those tools, because this, like a lot of players by then had tried to build web apps and platforms to do only one small part of the job. And just ask like analysts to switch context every time they needed to do one thing. And that So didn't we said, okay, now with AI, it's the right moment to build an integrated solution where they spent honestly, most of their time, like eighty, ninety percent easily outside of emails and meetings.

So that's what we did. We really built like kind of the first layer of what we sell today, which is like those agents that can do the job of an analyst until the last mile. And that last mile, what is really the hard thing to crack, is how do you make very nice graphs, like think cell ready graphs? How do you really are able to apply a client format to a deck when the slides were in your host style? How do you reuse LBO model from a previous deal, completely update it without breaking any formula, and with bringing a full traceable UX to the user so that he knows exactly what changed.

All sources need to be traced so that he trusts what has been done. So that's, I would say, the first six months of what we've built and what we've started selling, now, was entry point in this industry that made us a bit, I would say, very popular amongst the junior layers, but now what we're doing more and more is the next step, which is the collaborative layer kind of operating system. Now it's like, okay, how do you now make that full team of analysts on that project work together? And so we use the capabilities of each other. Like imagine you have a new journal joining, you can just use the, if he's in private equity, they need to do reporting every quarter, he can just use the skill and formats of his colleague and do it directly correctly.

And that means we also have a platform to orchestrate all of that. And that also includes the more senior layers. Like if you're a manager or MD, whatever, you can just ask specifics, what changed compared to last time? And they will say like, this and these assumptions changed, so you need to update the client. They will ask that typically five minutes before the client meeting, right?

And they'll get like, okay, this change, so the valuation has dropped and you need to check this and this with the client. Or if if you're an FTD player, we still don't have like this and this cuts of the data, you should ask for it because we are blocked on that certain work stream. So how do you orchestrate the full work and include the full layer of people within one platform. That's what we are cracking now and making a lot of progress. What it means in terms of products, it means we now integrate in SharePoint, emails, OneNotes, etcetera.

So how do you really make sure that you can become AI native as finance or strategy player, right? How do you have a single platform has all your context? So it's both memory, but also collaboration that you need to enable. So that's the big focus now. And then the big vision, and if you do that, if everyone, every player in the deal making industry and strategy industry can collaborate within these firms on your platform, then the doors are opening to basically enable collaboration across firms.

Because if you simplify it a lot, this industry is just, everyone gets a cut of the data, needs to do a series of analysis they're good at, because they've done it 100 times and have the right MDs partners that know perfectly the best practices in that market. And then they need to present it nicely, either in a PowerPoint deck or in a nice word memo. And that's like everyone in this industry, the FDD player to the investment bank, to the PE, or to the McKinsey that is doing the commercial due diligence, is just analyzing one cut, building a conviction kind of story around it, and presenting it. So if you can then, the third step, and that's really the golden goal, collaborate between firms with agents and humans, because a lot of these tasks, an ID will send an NDA to a PE, now a human needs to review that. Actually the PE, they have reviewed thousands of NDAs.

You can have an agent that reviews that, and then just have a human that signs off. So you can really like make them work together, also replace data rooms, because in the end, data rooms is just giving the right permissioning to everyone to see the right cut, but you can directly do that in a platform. And then you can actually create market liquidity, what I was saying before, right? You can really enable IBs to look at deals, like try to win mandates for deals that are smaller, that they couldn't do before, piece to look way further on the deal set. And that's really, I think the goal where we're aiming to go to.

But that being said, I really think layer one and layer two, you need to ace those two before you get there. A lot of payers salts in our industry are going to be the network for deal making standalone, but you don't create a network if you don't add the value before. So that's a bit why we are going we are not directly doing step three.

Bart: And for it is step three, which is really interesting, like the third layer is really like a collaborative environment between firms. For example, for a data room, and and and for the the listeners who don't know what is a data room, it's like an access control repository where all the aspect of a deal, such as the financial, legal, or operational documents are stored and organized during a due diligence of of a deal. Do you plan, like like because what's interesting right now is, like, the the human interaction and the and the and the the agent interaction. Like, It could be really efficient also for the agent of external parties to go in this third layer and the agent of another company to have interaction between humans and agents, right?

Severine: Yeah, exactly. Depending on, if I take an example, right? So an IB will send an NDA to an investment bank that has a mandate to sell a certain company, will send a first teaser to a lot of potential buyers. And then, so that's often not always a bit anonymized. And then the next step for everyone that's interested, they will say like, for example, a PE firm, can be a corporate as well, but let's say PE firms that are interested, they will say like, Hey, yes, I'd like to know more.

First thing that will happen is an IB will send an NDA to that PE to keep everything that will be shared afterwards confidential. And that is typically something that is like, if you're an analyst in a PE, you need to view it. Sometimes the legal arm of the PE is doing it, but often it's something that an analyst will still do. It's always a bit the same, and actually an agent would be able, based on all the NDAs that you've reviewed before, to know what kind of NDA you accept, what are the red flags you don't want to see in there. And so that's typically an example where today, people are already trusting AI to scrape the full NDA, check if they're red flags, and if they are, you have that third layer through that platform, you can directly actually send back to the agent of the IB, like, hey, look, can we just make this modification?

And then even before the human enters the loop, you can have two, three iterations, and then at the end, when we agree, you can explain to the human, like, look, we made those changes to align with your guidance on NDAs, and now you can just sign it, right? And this is kind of a simple example that people are already ready to do today with AI, it's just like you need to facilitate it, and now it's all happening over email. By the time we get to the full layer three, the potential of agents doing part of what humans are still doing today is a 100 times bigger than just reviewing NDAs, right? Like when the PE gets the SIM, so the data, really a long deck explaining the whole business, They're also like the agent of the PE firm that knows their investment thesis, all the previous deals they've done in the same market, etcetera, could already really do a good job at making the first model and drafting the first investment committee memo, etcetera. And that's also something people are already ready to hand over to AI.

Yeah, it's all about like, we need to develop those agents and then trust is being built every day and more and more tasks will be handed over to agents and what will stay at the center of where humans will add value is building conviction. If you're a PE firm, need to have a conviction about the industry state today, about what you believe you can do with it over the year you're holding it, and about the potential exits you believe in over five, seven years. And that's still something that you need to believe, it's it's it's and you need to believe it, and then you need to convince the full investment committee that that's gonna gonna happen.

Raphael: It's a it's a very interesting, nice, like, ambitious north star to like, the goal is to create liquidity in the market through, through this, multilayer solution. A very nice way of looking at it. Like, can you share a bit, like, where in the stage you are currently? Like, you're active for a year and a half. Like, how does the team look like?

How does the funding side look like?

Severine: Yeah. So we have 10, and the team is basically a lot biased towards tech team. Have eight engineers, including my co founder, CTO Max. And then we have two on the, let's say, more the commercial side, but even like on that side, everyone is really like AI savvy, right? It's Yeah.

The reason is what we're doing is new and no one has cracked it. So it's like less of a copier product, improve it a bit and go sell it to an existing pocket of money. It's really like, you need to innovate a lot. It's a lot of R and D what we do. So that's the team.

What did you, yeah, the time, like what we achieved over one and a half years. We basically are a US Belgian company, meaning we decided to move very quickly to San Francisco with Max in the very beginning. We kind of said, let's take the most ambitious route. And we thought like, if we want to be serious about this, most of our market is in The US. The biggest player in our market is in The US.

So let's go live in We decided to go to a self, because to really start the company, that's the best place to be.

Raphael: Both live in San Francisco?

Severine: No. So right now, our tech team so that was the first four months.

Raphael: Okay.

Severine: For five months of the company, but now the tech team is in Belgium. We basically take advantage of the fact that we have good tech network in Belgium. We're in Brussels, the what? The new tech incubator of of. So we have a good technical network here.

In terms of quality, it's crazy how good talent is in Belgium. Think in The US, it's so competitive and the quality for the same budget as an early stage startup, I don't think you get to the same levels. And so the strategy has always been like, after The US, we raise a bit of money. We create an entity in Belgium. We hire the tech team in Belgium.

And basically for the sales, it's just the cool thing, it's a very global industry. So they're all a bit all around the world. So they're a lot on calls, so you can basically take them from wherever. But I'm also traveling a lot, like going to New York, going to London, etcetera. And the plan is as soon as we can also having boots on the ground in New York and hire local people there.

And then I will be mostly based out of New York. Then now I'm also working a lot on projects, it makes sense that I'm also spending time in Brussels. So my own schedule is a bit hectic if I'm honest.

Raphael: I can imagine. The product that you were describing, like, is it already live? Like, people already try it?

Severine: Yeah. Already have paid clients. So mostly PEs, they were obviously faster than IBs. Now, some bit of IBs in the M and A advisory, you have a bit of, you have a lot of different roles, their FZDs are like financial due diligence players are very crunched. So there it makes a lot of sense.

And recently also, as we really have really good PowerPoint agents, it makes a lot of sense for consulting at large. So also entering more and more in that space.

Raphael: How long did it take you to deliver the first product, to go live with the first version of the product?

Severine: The very, very first product, like, do you mean live with clients or live like in our hands? Because we had something live in our hands after one month, let's say, right?

Raphael: Let's say with clients, yeah.

Severine: Yeah, that took a few months. March, I think we started March last year and maybe June or so, installation, because not necessarily for product development reason, but also you need to figure out how to install securely and with enough, like you need SSO, but you also need a central way to deploy via IT, etcetera. Going through that is a bit longer, which is normal, right? It's an industry that's And where it's non

Raphael: the first paying customer, like how did it come about? Like, was it someone that you knew via your network? Was it really outbound contact that you did or?

Severine: Lot of outbound, but it's like also warm outbound, The cool thing is McKinsey has a lot of people that are going to that, like staying in that industry. So you kind of have always kind of a warm intro and then figure your way.

Raphael: That's a fair point. There's a big alumni network, right? From McKinsey.

Severine: Yeah. Yeah. And everyone is staying in that in that industry. Right? Loved industry by by McKinsey people.

Raphael: That is a nice way, yeah, to get get entries in detail.

Severine: Yeah. You get the entry. They're often not the the buyers or decision makers, but they're really like, it's an industry, I really like serving this industry because it's an industry where people are really pro technology. Like one and a half years ago, everyone was saying, when we started, no one will trust AI, AirDrop is so complex, etcetera. And now they changed and no one is kind of, no one has a ego that says like, I will still not do it with AI.

Everyone is like, okay, woah, it's crazy how it evolved, give it to me. So that's really cool. And they're also like tech savvy, they wanna, like, no one is like, don't take my job kind of spirit. Everyone is like, yeah, please develop it faster so I can do the interesting stuff. Also we can grow bigger than our company.

Everyone wants to be leaders in AI and not the last one to adopt. Even though if you look at general, they were a bit slow. If you look at banks, they were a bit slow at start, but now they really understood that it has a lot of potential, and so everyone is really appointing head of AI teams, like tech teams gets renamed, rebranded to AI teams. It's quite fun to see that this has completely changed over the one year, one and a half years we've been in the market.

Bart: And also you are an ex McKinsey analyst building a solution for current McKinsey analyst, I would say. So, yeah, I guess that the your alumni McKinsey network knows the how painful it is to to have a thousand of iterations in a slide deck, or a due diligence process, etcetera. So I guess these are the right user. When they leave McKinsey and go to other companies, they can also buy your product.

Severine: Yep. And I think a lot is in the name as well, right? In the beginning, the name was a joke. Like we said, we want to have a name that makes it clear that we really do stuff and are not another web app that can retrieve data or just like spit out some text, but like that doesn't really do your job. And so actually it's fixing everything and please fix is a comment you get from a senior partner or from a partner, whatever, at ten in the evening with a lot of comments on all your back and you'll spend the night on it.

And it's, yeah, the name also helps a lot to, in one like, for everyone in that industry, it's a it's it's and a joke and a reality, so it helps a lot.

Bart: Yeah. The the the name is really cool.

Severine: Thanks.

Bart: It's it's really it's it's really, like, efficient. Are you are are you right now as co founders sending it to your employees, like you or Maxim?

Severine: No, no, we are. It's also not the same. Like, you're working with, like I said, right, we a team of ten and eight full technical people, right? It's a very different way of working than in a classic McKinsey team with the, it's not such a, let's say the big difference, it's way more flat structure, right? Like, if you need to fix something, you can fix it yourself or you, yeah, it's way more flat, right?

So, not really.

Bart: And I think that you mentioned that like your tech capabilities were in Belgium, while your more commercial capabilities are more in The US? Because I guess, like, this is also the markets you are targeting. Can you maybe explain why you still keep your tech team in Belgium? Yeah.

Severine: First reason is, we have a good technical network here. So in the beginning, it's really hard to find good talent. And actually, thanks to our network, that was way quicker in Belgium than anywhere else. When you reach the limits of your network, I still think Belgium is really relevant because of the quality of universities or education system and the cost of living in Belgium, right? It's crazy how we have, and it's also a mentality thing that has changed a lot.

Think we have really well educated people that now are like really ambitious and that don't all leave to The US, which I think is a bit of a change compared to, I don't know, fifteen years ago, where if you were the best tech talent, was no questions of staying in Belgium. I think that's not true anymore. And also you can, with a tech team in Belgium, have a global reach. So you can, I don't know how to put it correctly, but like play out that ambition while having your tech team in Belgium? And you also, the third reason is you also don't need a 1,000 engineer team anymore.

And so you don't need the pool. You need a really top notch team of, like now we're ten, eight engineers and we'll grow for sure, but not times 10, right? On engineer site, right? On FDE, like, and pure GTM, that's another discussion, but on pure engineers, you can do way more with less people, and big advantage of being a startup is that, I think. Maybe a last or fourth one is, in The US, so we're in San Francisco, people are way less loyal as well, like they just jump ships every now and then.

And I think if you're not one of the big labs, or one of the biggest tech companies, it's really hard to compete with all other ambitious startups. It's a good thing that we went there, that we lived there, because it levels up your ambitions. When you hear people speaking, they're like, okay, I'll also get to the moon, right? And that's super cool. But on the other side, the talent pool, need to imagine all the talent, they get pitched all those moonshot ideas every 10 times a day.

And so yeah, indeed they jump ships all the time, which is, as an early startup, not something that's very comfortable. There are going to be highs and lows when people that are in it and commit, right? And that's maybe also a mentality thing that's less that's in The US. It's easy to hire, easy to fire. It has advantages, but I see Belgium as the loyalty of people and as a strength.

Raphael: Nice. Maybe talking technical team, maybe that's a good segue to go a bit more in-depth on the product that you've built. So you explained already, like you're, you're, you're basically providing kind of a workflow to supercharge people that are in this M and A space. Right? And I assume that it's, that is like the traditional assets that they're working with, a Word document, a PowerPoint and Excel, you're providing tools to speed it up, to auto generate that, to do some calculations, to bring an Excel in sync with PowerPoint.

Is that a correct interpretation? So

Severine: first thing we've built is very simple. It's adding in Word, Excel, and PowerPoint, the place where they spend most of their time. And in the back, it connected the the context of each file, which we did the with each other. The big pain that we solved at first was like, actually you need to iterate on all those documents with Please Fix it, you keep them in sync all the time and we can do every action until the last mile in those documents. So that was a bit the first thing.

Now of course you can also, like if you save longer and longer skills, you can also just use the platform to do that and it knows you. So it knows your format and your previous model. So if you trust it already fully, you will probably do it without the add in and just do it in a platform, but people really want to see the result in front of them, like they're doing their job. So the whole upload, download a file, and we'll do modification that are black box, and then you need to trust us, is something that's not for, according to us, not the right entry point. So the entry point is really like, you just walk like, you just open your file like you would, like your previous model, and you just drop the new SIM and it will update the full model with a very big layer of control.

So every change will happen in a copy, you will be able to click compare both versions and every source that it uses in another file or in a uploaded file will be tracked. You can click on it, it will show you a preview of it. And so you, at the end, you need to accept everything. And then, so that's a bit how people still use Please Fix most of the time, right? And the more and more you build trust like that, and you also can leverage the power of having integrated so deeply, having, like going to the last mile so deeply, as you can then use that to now work asynchronously with users in the web app.

So use the power of the fact that you can populate PowerPoints exactly in that format with their, like, if you use action titles, if you put takeaways on the right hand side of a slide, if how you format your tables, what kind of formatting you use for a graph, if you can remember all of that. And then on the modeling side, how you like to model stuff, what are kind of Then you can do it more and more from the web. So in the future, I see more and more a way of people trusting please fix and needing less to accept, reject every change, still having that possibility, but going more and more towards end to end things being done. But that's a slow part, that's the people management, like the change management aspect, and that's happening, but slowly.

Raphael: So maybe to make the parallel a bit, like people that are working as a developer with a technical background, they do a lot of these things already in something like GlotCode or Codex, where they use skills and there is versioning control and they can automate stuff like this and they have, they can branch away and they can fetch data from another source and through something like Cloud Code, and then you're bringing basically this dysfunctionality to the financial role, right?

Severine: Yeah. Yeah. And also the whole so that so that's definitely how we started, but then also the whole collaboration aspect on on how you do that at the level of a team. Right? Like Mhmm.

Yeah. You can remember all the comments, all this, like please fix comments you got from last time. And then when you do a slide, it will tell you actually, maybe this is a better way to do it based on all the comments on knowing that you're working for this MD that prefers this kind of formatting. And so you can also get like a first time write quicker because of the whole team memory aspect. But yeah, indeed that's, we'll bring the same revolution, right?

A developer was writing code, now they're reviewing code. An analyst was writing documents. They will review documents and spend time on, does this make sense? Like a developer is now thinking like, is the architecture making sense? Is everything like, is it doing what I want in the end?

And for me and analysts, we spend way more time on, is this deal something we believe in? If not, what do we need to believe in it? Do we need to interview yet another expert or do we need to review our base case, whatever, right? They'll spend way more time on that instead of trenching things and kind of forgetting the overarching picture.

Raphael: Maybe a question, and I'm sure you get this question a lot on defensibility. Like, I'm sometimes a little bit involved in this field in a sense that I'm sometimes active as a adviser to do technical due diligence on on potential acquisitions. And I very much myself leverage very much Claude for this. So I record all the interviews. I summarize all of them, put sometimes put something in a model in Excel, something to a debrief on PowerPoint.

So a lot of these things are, let's say, in an individual's workflow already possible. I think it's hard to defend that part, even though there are definitely things that you can improve. Probably the strong value that you're adding is this trusted and auditable workspace, bit like Harvey does with legal. It's also very easy to do with Claude, just review a legal document, but it's very hard to do it in an auditable shared manner. Right?

Like, do you have the same vision on this? Like, is it the workspace that brings the value?

Severine: Yeah, lot. I think Legora is one of you mentioned Harvey, but I think we also really like Legora for exactly that. Shooting some product like maybe that they really ace is exactly what you said, right? Making sure that everything is collaborative within your team, traceable and trustable. That's definitely a big one on like why people want to use us.

And then I would say it's also the whole layer on the fact that you build more custom tools exactly for them, right? They have bit different needs than the individual that makes a slide. Can, doesn't really matter what format it's using. And so the whole UX is also a bit different and is opinionated for this industry. So I think that has also a lot of value.

Of course they could build it, right? I'm not saying that if a big bank doesn't put a few devs, they can build that tailored UX on top of cloud, right? But they will have to maintain it. Like it evolves so fast that they better buy and just what's best. The last argument is that's probably more an IT kind of sensibility the argument is what they are really afraid of when you talk with our buyers, like AI, head of AIs, etcetera, is the fear of being locked in, in a cloud, and then not being able to use other models, and having everything in one tool.

We use the best of all models, depending on which agent is doing what, we test that, we change that when new models get released. And that's something that they really like, the fact that you have everything centralized in one place, using all the best models without having to tell your analysts to switch every six months, because the race isn't over. And I truly believe the race is not over, even though cloud has really done a great job, but I also think open source models are not that far away from catching up. And so you could imagine a world where they say like, okay, well, you also have way more possibilities in terms of hosting. You could use your own API keys with us or like host open source model.

That's not yet the case, right? Because they are not yet there.

Raphael: Yeah. Makes sense. Yeah.

Severine: Makes from an IT point of view, your risk assessment, very risky if you just bet on one provider.

Raphael: Yeah, that's definitely a fair point. Yeah. And I would also say maybe, but maybe an argument from an IT point of view, I think the typical clients interested in this are also very particular or also very concerned about security. Right?

Severine: Like,

Raphael: if you if you can can show them a battle tested validated audited platform, like, that's that's worth a lot. Right?

Severine: Yeah. Mhmm.

Bart: It's true that things are evolving really fast right now, but it's it will be also good to have your, like, view of, like because you are on the field. What if not using PlayFix solution, what are finance companies using right now as a provider mostly? Are them using Copilot?

Severine: Yeah. So it has evolved a lot, which is fun to see, right? They're way more ready to jump and try things out. When we started, it was basically only Copilot. Like all Microsoft tools, it's of course a bit like in their DNA.

Then you saw a big wave of trying out individual add ins, like an add in for Excel, an add in for PowerPoint, like just trying to find out what's the best in all different solutions. Then they started seeing a lot of value in having a tool that's like integrated like that, that actually delivers on the promise of like having you not switch context each time you do something, because that's exactly the pain point, right? Just basic stuff, So they are like, that's a bit the wave we jumped on. And now it's really a lot on like, so a lot of tools have just built just Excel add ins, for example, PowerPoint bit less because it's way harder, but I'm sure on Excel, you've got plenty of those. That was a bit a big wave.

Then the next wave was, we came into that wave saying, actually we do the three. And that's now being one that's more and more like that people really like a lot. But the next step is of course, they want a system, right? They're now like, okay, I'm good with individual productivity. They want a full system that does things end to end.

There you in banking, I guess you know, Rogo, Hebiamb, in The US, Mobile ML in The UK. Those are kind of a bit the big ones.

Raphael: Do you consider them competitors?

Severine: Depends which one, right? So ModelML, yes. Hebattista and Rogo, they started from a very different perspective, we are definitely becoming more and more competitors. Even on yesterday, they started from a web app, pure, the first, I think, Rogo, the first thing was like AI for finance, like just a GPT for finance, just saying that it was more safe. I don't know how exactly, but that was the pitch.

And then they integrated a lot of databases like Cap IQ, FactSet, etcetera. So it was really powerful because you could just query it and it would actually retrieve the data from your preferred databases, but actually that's not where you lose most of your time. In the end, you still kept the subscriptions to all these So data it's kind of, it helps you, but it's not like really doing your work. Now they release something like Felix that is a bit more closer to really workflows like what we do, but they, for example, don't integrate in PowerPoint. They can generate PowerPoints, but they don't integrate.

So yeah, it's an interesting industry. We started from the opposite side, like, okay, we're gonna first really be in the work, like producing document layer and then workflow layer. They started the opposite way around. I also started a bit before us, so that wasn't possible back then. That's kind of a, I don't know if it's like or not, but

Raphael: Even though it's very easy to develop something, you now indeed see like the companies that started two years earlier that they are, that they already have a legacy, right? Yeah. Tackled problems in a completely different way than you would do today.

Severine: Yeah. See it as our current advantage, but it's not really right because this isn't over a year, it's disadvantage shifts to the next one. So yeah, let's see. That's also why we raised a bit of money. The whole plan now is to like, make sure we are exploring and basically getting into something that you can scale, right?

And now it's like, let's prove that we innovated exactly where needed, so really focused. Then afterwards we'll probably raise again or whatever, but then we only think about scaling.

Bart: On this topic, you you just raised 2,100,000 in February in a pre seed phase. It was led by PitchDrive.

Severine: Exactly.

Bart: But also with other VCs such as Syndicate One Yeah. Like really renowned in Belgium, especially PitchDrive, which is one of these VC known for in Belgium for their mentoring approach because it was created by people who personally went through the whole cycle of creating a company and then and then selling it. It's kind of smart money. Do you feel it right now with the different discussion you have with the shareholders?

Severine: Yeah, yeah, we love them. I think it's one of the best choices we've made. Mentors, definitely because of their mentorship, but also just their, I'd say even more like just their positive attitude. Are probably like the VC, we've met a lot of VCs, right? Our space is super competitive, so I'm not gonna pretend it was easy.

It's definitely like we need meet a lot of them and they are just like fully, fully, fully, fully supportive. I think Syndicate One is really the same. It's a bigger network of operators working outside of Syndicate one, right? They all have other jobs. So it's not the full Syndicate one that feels like that, but we have one or two really, really supportive people from Syndicate one.

And Pig Drive, it's the same. They're just supportive in any way. They can, if you ask them, they're also super reactive. They really don't want to add I think that's exactly the right thing to do when you want to support funnels that early. Right?

They are just like, I'm just on Slack or on WhatsApp with them. Like that's simple. Then I have them on the phone if I need to discuss something on the same day. It's, yeah, of course it's the first time I raised money. So I can also not speak from, I've seen, I've been in deals with other VCs, but from now I really feel how helpful they've been and also how supportive they are when we have bigger questions, harder questions to solve that are not easy.

They're really not opinionated in their own advantage. They're really like, whatever you decide, we will support you. And that's something I think we felt from the first meeting, had two calls with them, think. And then I think the vibe, the energy was great. And then they just said, Okay, we'll see you in person.

The only VC that said, We'll be there tomorrow at your office. They came from different locations. They all were staying, and just came to us, right? While other VCs, they invite you, which is nice and painful at the same time, right? You need to go there, blah, blah, blah.

Like there, they just all came, and yeah, it was so quick, and yeah. This is just one example of how I like their their new bullshit. Like, just

Raphael: Let's what pitch drives so I know them a little bit, but in the VC space, especially in the pre seat VC space, they are they are very tech savvy, and they were very early with adopting fully AI native workflows. Really, they know what they're talking about, right?

Severine: Yeah, they're obsessed with it. It's so fun. Have bots running all the time. They all have their own mini. When Open Cloud got released, I think they were the first one to They're mess around with still really founders in their mind, right?

Like in their heads. It's really fun to see. And they kind of are in a mentality of let's break things and find what it like they're fond of. It's fun. And they have a, if you want to have them on the podcast, they have a lot of fun stories on things they've built and on Frankenstein's they've built as well.

That's the reality, right? And then they think about a few very cool use cases, they probably will be more than happy to explain it themselves, but it's fun to see that they're also breaking stuff, right? Which we are also. Right? So it's it's pretty good to

Bart: Alright.

Raphael: Cool. We had one. So you you you you raised what was it? 2 point two? Two point two.

Severine: One year.

Raphael: What are your priorities with it? Like, you're like, you were already explaining, like, ten ten people now, eight of them developers. Like, how do you see that going forward? Like, where is the where is investment going?

Severine: Mainly in the products, right? The whole point is now like, let's just have the very best, best product and then prove that we can scale adoption with our existing clients, sign new ones, but especially go to full rollouts, right? Like this is the hard thing in our market. It's a thing like, if you look at other startup ideas that for example, like ERP, right? That you will directly, everyone will have access, right?

Everyone needs that information. When you're in the productivity space, it's different, right? They will try everything out and not everyone will have, you won't do a full rollout directly, especially in this industry where it's hard to get in, etcetera. So that money will serve for proving exactly that by innovating and working together with our clients, like a lot, our engineers are also acting as FDEs, like forward deployment engineers, getting to a few big rollouts. And then you prove that if you then put another round of fuel in the engine, that you can you scale more on the GTM side, which we're doing also, right?

But it's mostly, mostly product and making sure you get the best product. It's a bit of a, I think it's also a European way of doing stuff when you compare like Gora and Harvey, which are like legal is a bit in front of the financial and strategy industry because it's text in text out. So really kind of a bit like code, right? Code is just It's a bit of a use case that's obvious for AI and also high paid shops. Which is the case in finance as well, but it's a bit harder in finance because it's math in Excel, then random objects on a two d plain white surface, it's a bit harder.

There is more objectivity on what is a nice slide looking like. So there, Harvey is really the one that went all in on distribution. And then I'm sure now with all the money they raised, they are doing great stuff at their product, but like you're right, where it started being obsessed on product and now the growth is, they are like accelerating faster. They're still smaller than Harvey, but they're just, their acceleration is faster. So I like it a lot.

This is also the reputation they have when we talk to a lot of lawyers. So it's kind of what we want become, the legora of defense strategy industry.

Raphael: That's a nice goal. Yeah. Yeah. I'm wondering, like, over the like you're doing now a year and a half, if in that time frame, like, your view on go to market priorities changed? And why I'm saying it is that I noticed, like, especially the I want to say in the last six months, I noticed that, like, a lot of partners at funds are very nervous about the portfolio companies that they already have.

And because of that, like like what is the defensibility of them in this AI era? No one knows. Right? Like, leave it in the middle how, how significant that is. But, and that also, I think, like turns into being more cautious when doing new investments.

And where I noticed this is very subjective because this is just my own findings, is that at least in Europe or Benelux maybe even more specifically, is that we see these, whether it's pre seed or seed or series a, like there needs to be an earlier proof of a successful go to market. Where before Indeed, there would be this consideration, Ah, there's AI in it. And like, it's a good business idea and there's a good model and there's a good team behind us. We're going to invest until the product is perfect. And like, the feeling that this is changing a bit, like the balance Yeah.

Is

Severine: It's the thing that becomes incompressible while in the past that wasn't the case, right? In the past, what was hard is building a good product fast with good engineers, etcetera. Now that is, it's still hard to build like with the right traceability and like the right-

Raphael: True. Yeah.

Severine: And the exact right UX and etcetera, etcetera. It's like not, it's become, it's faster to build stuff, but it's not faster to land the perfect product that your user wants and then make, like for us, it's a lot of, it's new, right? So it's also a lot of change management and that's just humans again, right? So that side of product deployment and adoption is still slow. And then, so that's still a bit incompressible, but it's fine, I guess.

It's the same for everyone. But if you compare like the VC game, they're just comparing opportunities, right? What is really still incompressible is like your execution engine in like, yeah, GTM, right? And there, I think, indeed, you need to show that you can do it fast. And when we raised our round, also like, I think we managed to impress a lot of VC that after only being six months in the market, we talked to the biggest players of our markets.

We entered that all the clients that Rogo was in, in New York, right?

Raphael: That was impressive.

Severine: Not that we signed them, this, it takes a lot of time. Rogo also took more than like a year to sign them, but we managed to enter in the same rooms, like all the head of AIs of, I don't know, the biggest banks, Lazard, the Rothschilds, the Rothschilds, Stiefels, the Mullis, where Lazard started to We also entered there. And so I think being a six months old startup and going to those those Yeah.

Raphael: That's impressive. Yeah.

Severine: In person meetings was was a nice proof point. It doesn't mean you crack the market because it's those clients take a lot of time to close really close. Right?

Raphael: I I I've done a lot of I've done a lot of b two b stuff in the past, and I think the the actually, the hardest part is is getting the door to open. Right? Like Yeah. Being able to talk to the right people. And then it's normal that it then takes, I don't know, six months to to get something like this to fruition.

Right? But it's impressive that you're already having these discussions.

Severine: Yeah. So entering in the room, showing that you can enter in the room and that's the biggest ones are listening is what you need to do. But then I think, well, in the end you need to, if you can't build the right product, it doesn't matter, right? So if you can only sell a nice vision, you'll quickly, another founder will do the same, right?

Bart: You touched a word on that, but maybe I would like to go a bit deeper. So your solution is changing the way analysts, engineers will will will work. You say that it will free up some time for them to become expert in other fields, in other tasks. Did you already think how your solution can drastically change and shape the new workforce? What can juniors do other than doing slide, if I put it like that?

Severine: So overarching impacts for the industry at large is creating more liquidity, but then on the individual level of like analysts, etcetera, I'm not a strong believer that the parameters will completely change. Like I still believe that the MD in a bank will not orchestrate a bunch of AI agents. I believe that the pyramid will probably shift a bit because the base is probably a bit too large in some cases, not everywhere. So that will probably like rationalize a bit. Right.

And yeah, but I still believe that it won't completely change. It's a bit like my macro perspective, but then at the very, like what will be the day of those people? I think they'll have the same revolution as software developers, right? They really review documents instead of spending time creating those, finding the right data, and then comparing, copy pasting, update like, and then also losing time updating the other team members, etcetera. Like all that will sit in one system.

And so they will spend time, way more time thinking about building conviction. Do I believe in this? And if not yet, what do I need to believe in it? And the answer to that will often sit in, okay, I need to talk to one more people of the XCO. I need to one more person on the XCO to ask those questions.

I need to go interview one more, I don't know, supplier of the company. I'm looking to buy one more expert in the field. So all those things, like how you build conviction for me, it's like getting more answers and getting, then you can refine your models, but you will not crunch the whole update. You'll just tell them, okay, actually the growth rate is not 5%, 3%, please update everything. And that's how you'll build conviction.

And so I really think the big shift for me will be, you'll spend way more time on the relation side of things, which actually is pretty good news, because that's also where humans are good at, right? Like creating a conviction, building a relationship. And so I also think the skill set will be really different. You'll need to be very smart to understand fast, and then you'll need to have a lot of empathy to quickly build relationships and get the info you need for building that conviction quick and and in a way that is defensible, like in front of a full IC. So you will still have to be able to explain everything.

So you need to understand everything until the last assumption. You'll be driven by all the human interactions you had to build this and not all the nights you've spent on copy pasting stuff. Yeah. So I think that's a bit of an individual level that the job will change.

Raphael: That's interesting to hear your vision on because it's also very parallel to how software engineering is evolving, right? You used to be a software engineer in this specific niche. Let's say you're a front end engineer. Doesn't really exist anymore. Like, these roles are converging into into your your next back is is, like, expected to also do back end.

You're expected to do some data work, etcetera. But you're also expected to to and that's maybe a bit what you're saying, like, the relationship part, expect to be much closer to the customer to understand what value are you creating to the customer and to be able to shape that into a creative solution. I think that indeed, the good people will be able to, are they able are the people able to understand other people. Right? And and to Exactly.

To express that in in in what is needed.

Severine: Yeah. For me, the the, like, the EQ is is is probably

Raphael: Yeah. Interesting point.

Severine: Be valued more and more in which I feel like is a is a very good thing.

Raphael: Yeah. Interesting point. Thanks a lot, Smeets. It was very interesting to hear your your views to to to understand what Police Fix is doing. What is the best way for people to follow-up on you and PleaseFix?

Severine: Follow us on LinkedIn. I think that's also where most of our industries is spending scrolling scrolling on. So LinkedIn, and that's you can just add me on LinkedIn or follow pleasefix.ai. And if you're interested in seeing the product live, you can always book a demo on our website, pleasefix.ai.

Bart: Good. All right. Perfect. Thank you very much, Smeets.