How Trampoline is Changing the RFP Game with Edouard Reinach, CEO & Co-Founder

Nectar: Pleasure to have you on. I think a natural place to start is I wanna talk about trampoline, right? I’m obviously familiar with your startup and, very interested to learn more today. And like I said, this podcast is a good excuse to, to poke and prod, maybe walk us down the path of the Genesis story.

So what led you to start this company.

Edouard: We started trampoline, it’s a convolution story because we started with something else before. But basically, we realized that, what we were working on was really great, to help people, respond to RFPs.

So that was, what led us there. We also decided to tackle the challenge of very complex RFPs. we know that some RFPs are answered by just, copy pasting a bunch of stuff. And you can create a new proposal, but a lot of the time you need to gather the knowledge of six people and project manage them toward a proposal within a few weeks.

That process is super, hard. It throw their wrench into people’s work and we felt okay, like we can’t help with the technology we have, What has become a trampoline, as we’ve started getting customers using it, we realized that, it’s really good at building proposals, but it’s really good at a lot of things in the context of a business where you need to have a lot of people coordinating themselves, on a project that is not necessarily super core to the business.

Lots of businesses have lots of side project. They’re not necessarily bad, you just need to do them. And our AI is a like chief project manager for you.

Nectar: Yeah. Interesting man. I know a little bit about this space, just having, run a professional service firm and then like we get this, thing called non RFP, we have to, spend, 30 hours.

I remember it was like answering RFP was just this god awful long process. The documents are like 15 pages. Very opaque. As a process too. ‘cause it’s like on the other side, you’re not sure exactly what you’re looking for. So yeah, maybe walk us through then, like how the platform you’ve built injects the RFP and then, like kind of time to value for the users.

Edouard: Yeah. So the way it works is that, you upload any kind of document, so an RFP, let’s say 300 pages, Within five minutes, you will see the PDF that you uploaded, or the Excel spreadsheet, And you will see that on every important part of information. There is a highlight made by the ai.

So you will see that the document has been highlighted just the way you would with the highlighter. And every highlight is creating an action item. That action item is, spread on a convent board. So it looks like a Trello board or Java board. It’s basically an agile methodology of entering to an RFP.

And so now each action item, you can basically talk with the AI and say, Hey, can you assign all the security question to Nectar? So the idea is that within 20 minutes after uploading this document, all the questions are assigned to the right person, and each person receive all those questions, but they also receive a AI generated answer.

And that answer has been generated on past answers that they’ve provided, past proposal that you’ve made. Which means that the only work that they have to do is correct the AI answer, enrich it, and ultimately validate it. And when they validate that answer, it becomes part of their knowledge base. So basically the work of answering the RFP also triggered a complete, update of whatever posture or strategy or discourse they can have.

Around their, service proposal.

Nectar: Yeah. That’s really interesting, man. So it’s yeah, it seems like obvious hindsight, it’s why didn’t this exist? Five years ago when I was answering RFPs and sweating on weekends with my team, it’s we gotta work on this.

Yeah. But it’s also such a broad space is are you tackling it in any particular vertical? ‘cause I can imagine like a lot of. Government entities tend to be the ones that issue RFPs, but then also mega Corps are the ones doing it. Like how do you approach then the go to market? I’m gonna bounce back to product, but curious because that’s the first thing that comes to mind.

Edouard: So for every, public rfp, like public tender from the government, there are like eight privates RFPs in the market. yes, it makes sense to go after like companies who are like heavily invested in bidding to the government. But there are lots of companies that I know of who don’t bid too much with the government that, but they bid a lot on private, tenders, private RFPs.

So it doesn’t matter to us. What matters to us is that you’re probably a service company or selling to enterprise, which means that you’re not doing. RFPs for relatively simple services. You need to propose a differentiated way of offering your services. So you may have a methodology, but what you’re selling mostly is expertise, talent.

And when you do this, you cannot just copy paste stuff and put them into a new proposal. You really need to show that you have a. Precise insights on that market, on that industry, and that you’ve done it before, or that you did something similar in the past and you could do something better this time.

So it takes a lot of time to articulate this into a compelling proposal that people will read. Our system really shines in those situations.

Nectar: Yeah, it’s fascinating man. It’s fascinating. What about the value on the person receiving the RFP? In terms of your product, right? Because it’s I imagine, the team that’s prepared it, you accelerate the time, they’re able to do it in I don’t know, let’s say 10% of the time.

I’d be actually curious to hear you on that KPI, right? Like how quick, how much time you saved by the team, right? And how much better quality, but then what’s the value for the end user that’s resuming their fp? Have you thought about the product experience? I imagine they’re not the ones touching the product at all, but curious to how you think about it end-to-end.

Edouard: our system helps the people responding to the RFPs. Eventually we’ll also be able to help people writing the RFP just for the RFP response, we save our users between 60 to 80% of their time. the first time they use it, it’s more like 60%. After a few months, it’s gonna be more like 80% because we have a machine learning algorithm that learns from every interaction in the system, and that makes it optimized to your specific business.

So the end users, they’re like three categories. they are the response manager, proposal manager. the person leading the charge on, okay, we need to answer the RFP They are like a project manager, but not really. And they usually hate project managing that stuff. So they’re really happy because this is a very simple software where they can just talk with the ai.

JI does a lot of things for them. Then we have the experts, so the subject matter experts. Those people are like the most busy people you can find in the business. They really have other shit to do than spend time on RFPs. There are usually. Very high worth, billable, hour people. So you need to protect their time.

Those people, they really love this system as well because they don’t have to repeat themselves. They just need to enrich the content that’s already there. Then you find the compliance people, so legal, the content writer. So the people will ultimately write the proposal. They just love it because they are for sure they will never forget anything in the system that we’ve built.

There is no way you forget about any requirements or any, compliance, matter. So those are the three kind of, end users we have.

Nectar: Yeah. Yeah. And so far, what’s the response been from users, right? Because you mentioned about a pivot, really focusing on RFPs maybe gives, two part questions, like sense of scale today and share any metrics, public metrics that you want.

And then secondly the value that you know the customers are seeing so far.

Edouard: Yeah, sure. So basically we, right now we’re focusing a lot on the quality of life of our users. Because we think that this is a good heuristic of knowing if the product is good. Like it’s not just about statistics, time saved.

It’s also do they like using it. And one thing that we discovered is that the product. Shine very bright when it comes to remote teams and distributed teams. So we’ve had, like most of our customers right now, have multiple offices around the world. And that’s really interesting because it is true that when you are answering to an RFP right now, you will have to organize meetings with everyone involved, like the seven people team or the 10 people team that you’re assembling to answer.

You’ll make meetings. So first you’ve already lost a week just to organize the first meeting, right? With our system, within 20 minutes, everybody’s been dispatched to work. So that’s something people really because it means that they’re not, there, there’s less obstacle. On the journey to, respond to the RFP.

One thing that we got from one of our customer in Montreal, he told us this is the first time in my life that I receive an RFP and I don’t have to worry. I don’t have to stress, I don’t have to call my wife and tell her that next week I might be working late on evenings and maybe I will have to work from the chalet on a Saturday.

Because now that they have trampoline, they can work on the backs size format, right? It feels a little bit like a Duolingo like you, you can just go in, work for 30 minutes, go out and it’s fine. Like you don’t need to block four hours of your time, to or reorganize our entire schedule for the next two weeks.

Nectar: Yeah. Fascinating. So it’s like I really see that, like going back to shit, I wish I had this before. And then in terms of in your value prop, do you, have you seen any, like better close rate from the customers? Like the ones that are using your platform?

Are they closing more deals versus I imagine, the company’s doing it the old fashioned way that take more time to answer. So what are the main drivers that. have success for an RFP? Is it like the time to response? Because usually it’s everyone’s deal the deadline.

Is it quality of the answers? Of course pricing is the big one, right? But I imagine that’s not a variable you’re touching. Double clicking here a little bit more on the model.

Edouard: Absolutely. We don’t have enough life, like we haven’t been in the market long enough to say that.

What we do know is that they’ve all been able to respond to RFPs way faster than they used to. With less stress. They’ve all won, lots of tenders already, Another cool thing is that sometimes they get bits where it’s explicitly said, do not use AI to answer that bit.

And because everything in our system is architectured around a human in the loop elements, like there’s a human everywhere. So you know that the AI is never hallucinating anything. They feel very okay with using our system and saying It’s not an AI like writing the document for you. It’s an AI gathering the knowledge for you and helping humans do the work.

Finally, I think, the most important thing, is that we have customers who started doing more RFPs as a result, so basically saving their time, allowed them to bid more on like opportunities that seemed a little bit more farfetched.

Now they’re like wealth. The trade out, like it’s worth the time to just chase this opportunity just in case. And they actually won a few that we know of. So that’s also interesting. It means that at the same conversion rate, if you start doing 20% more RFPs, the net gain at the end of the year is actually tremendous.

Nectar: Yeah, that’s a fa fascinating man. And like, where do you see your product evolving? I know it’s been, you’re two and a half years into the journey from what I understand. So how do you see the roadmap? How do you see expansion, of what you’re doing?

Edouard: So right now, I think what gets.

Startup kill is the lack of focus. So we’re trying to stay very focused on this very core use case, which is, hey, like the journey is very clear. you get an RFP, you need to engage all your subject matter experts as fast as you can. Have them, certify the content and then write the proposal.

Right, and you can do this in a few hours with our system, there are two remaining parts that we’re working on right now to say that this core use case is feature complete, for us to expand the use case. One is tracking change, requirement changes. So sometimes a lot of time when you’re working on an RFP or any projects for that matters.

Requirements change, right? So stuff that you started working on, you end up not being true. So you need to revert some of the work. So imagine that you have this trade board and you have 80% of the.

Of the cards completed and then you receive an addenda or you receive a new information that changes stuff.

We want to be able that some cards go back into the account board so that you know which cards are like. Linked to those changes so you can revet the information and make sure that everything is fine so that you can produce that, final document. So that’s one thing that we’re working on. And the second thing is really, yeah, the ability to load a document and tell the ai this is.

What we want to do. So it’s not an RFP anymore, but maybe, it’s a different thing. Okay. It could be like a annual report. It can be, yeah, like we, we have a lot of different use cases. One of which the most requested of them is, Hey, we are a city council and we need to write RFPs to go to the market.

Can you help us with that? And we can. So we’re thinking of all those, case use case and yeah, like enabling these with our system.

Nectar: Yeah. I was gonna say, we could probably riff on a bunch of like neat ideas and, like how you use agents, et cetera. Maybe I guess then the natural path, since you have an expensive vision, but like you said, focus for now.

Do you see yourself going down, the VC route? Do you see yourself like trying to raise capital to go big or you’re curious to hear you even though it might be a bit precocious,

Edouard: we’ve already took VC money, so we’re fairly much committed on that route.

For me, just VC money has to be for one purpose only and it’s really fueling an engine that’s working. So right now we still think we’re PPMF, like we’re signing new deals, twice a month

I’m very skeptical with it takes a lot for me to be like, okay, this is an interesting, like we feel cool and it’s consistent and everything. So I’m still a bit. Worried that we might not be, PNF enough or that, it’s not, the value is not explained well enough maybe to, but yeah, as soon as we see the growth being more predictable, VC money is absolutely the route for us, especially since, as we expand on the use cases that we support, there is a little chance I think that like in our ambition that we could become the next Trello. And yeah, that’s something I want to chase. I want to try to become that,

Nectar: Yeah. You mentioned Trello because it’s I was watching your demo before and it’s hey, it’s very similar, similar in terms of use, but also you’re building something that’s AI native, right?

So I think there’s some cool stuff that you guys have built into the product. In terms of you go to market, like you guys have built like a really neat, lead magnet tool with, I’ll let you speak about it, but how much of it is inbound, outbound, today, and how do you see that evolving, in terms of your sales strategy?

Edouard: Yeah. So we really go after companies, who have, pre-sales people in their teams. So what we realize that if you don’t already have hired to manage the complexity of your sales, process, you’re probably not a good fit for us.

It’s important to realize for people who don’t know about that, but, right now, like in most enterprise deals and service deals, 70% of those deals on the presales team. So on somebody who wants to have an opportunity define what the work has to be. Define the scope of the work so that you can come to pricing, with the conviction that you have, good margins and room for errors, so that you can have a good relationship with that customer.

So if you haven’t hired somebody to do this, you probably don’t have a very complex RFP response process or a. sales proposal process. So that’s really where we are.

Nectar: Yeah. And then I wanna maybe take a step back and talk about your journey as a founder and as entrepreneur.

So it’s like why go through this hiring journey? Again, you’ve had good positions in the corporate role, even though you’ve been very close to, The entrepreneurial scene for a long time. So Why start a tech startup?

Edouard: I’ve been an entrepreneur like half of my life, but more in the service industry before.

I’ve always knew that building a product was the hardest thing. Like building a service can be. I’m not saying it’s easier, but it’s easier at the beginning, for sure. Harder to scale product is, the opposite maybe. Once you have a really good product, I feel it’s a bit, easier.

So yeah, like to me it’s a challenge. Like some people like to run, trails. Some people like to go, at the Everest. I went the entrepreneur route. So for me it’s a challenge. Like it forces me to be more disciplined every week, to really like. Engage fiercely, in, contrarian ideas.

Look at the market, work on my fears. So yeah, that part is very interesting to me. And in the end I also feel like, I’ve always wanted to contribute to the society, anything. And I’m happy that the RFP problem found us because I worked on a lot of them. And yeah, the process is absolutely disgusting.

Like it’s really hard to do. And, so yeah, if I can help businesses do this better, I’m so happy I can.

Nectar: Yeah. I like the openness on, some of the scars, et cetera. Can you talk to us a little bit about, the early days of trampoline? how you came up with the idea?

I know you’ve, yeah. You’ve switched from a more broadbased use case to RFP specifically. I don’t wanna use the word pivot ‘cause I just find it’s Yeah. But it’s hey, you wanna find value. So yeah. I wanna double click on the the, yeah.

Hey, how do we come up with something that creates a lot of value for people? Do you mention of solving a societal challenge? Yeah. And then, on that journey of, okay, we have to steer the car and sometimes we take a, left turn or right turn. So you the click what went through on your, in your head there.

Edouard: So we, yeah, like we, when we started trampoline, the idea was, hey. Now that there are ais, like AI, liket are very good because they’re trained on a lot of knowledge around the world, right? And if you want to use AI within your business. It’s gonna be not so good because the AI hasn’t been trained on your data.

So the first thing we did was, hey, can we create like a search engine so that you can find any information in your slack on your teams? In your emails, your drive, et cetera. So we did this, we started working with design partners and after eight months, we had a functional alpha working. We put it into the end of users, and we realized a few things.

The first thing was that the rates of utilization was about five times a week. Which was, it didn’t seem much. I’ve learned since that actually that’s what lean.com has. Like people use it five times a week, which is a bit weird for me. But we, at the time, we were like, that’s not enough. The second thing that happened was that, we saw that the users were looking at the search results and.

Either they were like, I’m not sure those are the right results. And when we were asking them, did you expect something else? They were like, I don’t know. Or they would tell us This is a good result and I understand why it stopped, but it’s actually not the right document. And what we realized is that actually, and yeah, and the last thing we saw was that 99% of the documents and discussion and everything were never.

Which means that you’re basically searching, like indexing a lot of data that you don’t reuse it. But one thing that we saw was that at some point we had two companies back to back, started using our system, like 17% more for just one week. And we’re like, what? What? What’s going on? Why are you using it so much?

And they’re like, oh yeah, we just got an RFP. And I was like, oh, interesting. It is true that an RFP like triggers a big search for a lot of information in your company. I’ve been there. And so I was like, okay, that’s very interesting. There’s something to be done around that. And also one thing that they were complaining about was that.

We were supposed at the time to have a system where if the search results are not very good, you can use an AI agent to ask someone in the company what is the right answer to that question. And the idea was that over time you would basically, augment the search results with what other people in the company had said about that question.

And people really like that. So we went back to the drawing board and we said, okay, if it’s a you’re, you are going through a process of gathering a lot of content, maybe we should not be tapping into Teams Slack Drive because that information is absolutely not useful. Maybe what we should be doing is just ask people what is the right information, and over time leverage AI so that they’ve never repeat themselves.

They just enrich whatever it is said two months ago about something so that the information is always the best it can be. It’s always validated, so the AI does fewer and fewer hallucination over time. So that’s what we went with, less year.

Nectar: And you also don’t have to train your model on data that’s not as relevant, like email or Slack You save a bit of compute on that front.

Edouard: No, exactly. And it’s like people tend to think when it comes to search, they like the bar is very high because they think of Google what they need to realize that Google is really good because the whole world is writing for Google.

When you publish something on a website, you make it optimized for a search engine. So 50% of Google’s job is already done for them. The second thing that is very important, Google, is that Google is looking at backlinks. So they want to know how many websites are sending traffic to another website, and they use this as a heuristic.

So as a way of determining that this website is authoritative. essentially the idea of Google is that a lot of humans are manually saying this is a authoritative website such as Wikipedia. So the way we do this with trampoline is that since it’s impossible to have this heuristic in a business, we will ask people to select manually the documents they should be using so that the AI can have the best knowledge.

So when you do this, you have the best of both world. You have a very fast AI that’s using the best information it can with the knowledge that this information is accurate.

Nectar: What’s mostly misunderstood in your view about enterprise AI today? There’s been a lot of, let’s say, memes about it.

Oh, it doesn’t work. There’s a lot of people experimenting. But then you see the value and obviously if you’re vibe coding a CRM, maybe not, but there’s so many different, parts of the stack you could operate on. What do you think people don’t get? ‘Cause you’re so close to what’s going on.

Edouard: It’s really like a matter of expectations. A lot of people think that, tech is really good, because it’s trained on billions of data points from the internet, but it makes it also very generalistic, right? it basically has a universal. Knowledge base. When you put it into your business, it’s gonna be generally good.

But then it’s gonna be very bad at things that actually makes your business unique. What makes your business unique is where actually general language model will suck, okay? Because they will not be as good as understanding what makes it so unique. What makes it so special? a lot of people expect that.

The AI can now, act, as an all-knowing entity in my business and do stuff that humans don’t like to do. And it’s not gonna work. You will need to fill it. Better data. It’s still a data engine. Like the data you give, it determines a lot the outputs. You get garbage and garbage out. giving the AI better knowledge, better data is actually super hard. There are no ways of doing this really well unless you use tools like trampoline because. That’s what it is. Like trampoline is Exactly. We call it a context capture, a system. Like we try to get as many information as we can from people, because that’s the thing, businesses move very fast.

So you cannot ask an AI to know that between this morning at nine and tonight at five, you had 15, 20 discussions with different people that cannot refine what you think you should be doing for this project. AI has no access to all the discussions. And even if it had, even if it had listened to everything that you’ve said during the day, it doesn’t know what you filtered in and out.

It doesn’t know any of that. So even if you have this AI recorder on you. It’s still not super valuable. At some point, you need to, stop yourself and talk with the AI and say this is what I’m thinking now. This is my new thinking about this stuff. And because you need to make a judgment call.

And a lot of people right now, when they look at an ai, they expect the AI to predict what should be done and also have enough judgment to decide on what should be done. I think that the judgment part is essentially human. It’s not a machine, like a machine cannot do it because a machine doesn’t have, the consequences, right?

Like it’s really hard to make good judgment calls if you don’t suffer the consequences of your judgment calls.

Nectar: Yeah,

Edouard: and a lot of people’s judgment goals in an organization nowadays. Have a lot to do with the anticipated consequences like they’re thinking about themselves and their teams, right?

So yeah, like you, I think people need to realize that the AI has to be a tool, but there has to be a human in the control tower, right? no more humans plane. But you still need a lot of humans in the control tower.

Nectar: How do you use it for yourself? Either personal or work?

How are you using these new tools?

Edouard: Oh, wow. I use it, in many ways. Of course we use trampoline for ourselves, so every time we get a security questionnaire, when we do grant applications, we use trampoline.

I use GM Loop to create a lot of, workflow automations. for example, like our entire, security compliance framework, is. Like we, I augment my work with, so for example, every quarter we have a meeting with all the engineers where we review everything security related.

I basically have an agent that listen to that meeting. At the end of the meeting, it does the minutes of that meeting, but it also triggers a few AI so that my risk register is updated. I have a lot of databases that keep track of our compliance posture, that are automated using this.

So this is really a huge time saver. We also, use those agents to help us with sales. So we have a lot of sales augmentation, workflows. in personal life I use strategy quite a lot. Our team is using cursor and the best models, to code trampoline.

Nectar: Yeah, there’s a lot of talk these days, especially on, on Twitter, about Hey our employees need to be AI native, and it’s if you’re not using an AI tool or you have to prove that you’re using it.

Maybe the broader question here is you’re building your startup as wa how do you think about culture? Given that you’re AI native, in, in that sense, you’re building an AI company. does it change from the good old fashioned company building? What’s different, what’s the same for you and obviously in, in your own company?

Edouard: The cost of trying out new thing is lower. For example, we, for our go to market, we worked on a tool that is called Go No Go. It’s a free tool. It’s something that in the past I would’ve probably not done because putting code behind the marketing system, it’s overkill. But now that it took us like.

Just a few weeks to basically make it available to our customers. Like the model behind we already had. That makes it very different. Like all of a sudden you can create value very fast for your customers at places where usually you’re like, nah, I’m not gonna do that. So yeah,

We also change the way we design things. So we have we try to have a very, like I don’t wanna call it a radical ownership, but it’s like that. So if you’re building a feature for trampoline, we tell you what’s expected in terms of outcome for the user, and then you are working on it.

And, we don’t ask you for a wire frame. Like we don’t do anything in, Figma. you just start doing it using your best intuition. And once you’ve done a big chunk of it and you think that, it looks good. You will show it to us and then we will, challenge you on a few things that you did, and based on that feedback, you will come back and say, it’s almost done.

But it’s very interesting because basically it means that now we are hiring people who have actually good judgments. So culture, if you think about culture, for me, it’s really hard to have a AI native organization if you don’t value everyone’s judgment. And if you don’t encourage people to make more judgment, call faster.

Because judgments is what makes everything slow. The AI is doing predictions, like it’s saying this is the code you should be doing, and then you need to say I accept that we called it this way, or maybe we should code it another way. So yeah, a lot of organization are organized differently. They don’t like employees making judgment.

They like employees following rules. And that’s a big problem. If you have an AI native organization, it means that. Yeah, it’s just rules and code. And at some point there’s no human emotion or anything, which means that your users, they will think you are. Completely crazy, shipping those things, that clearly no human has reviewed before.

Nectar: Yeah, that’s a pretty good answer. so you said like the things that change and the things that don’t change, so it’s like people do not, like you mentioned, the value of human judgment like that’s not going away. So I find that’s a critical piece.

Edouard: It’s a critical piece. And a good judgment is rare. Like most people don’t necessarily have good judgments. And if you just rely on an AI to do your work, at some point your competitor will do the same. And so what we will have three different companies using the same AI in French Point, from endpoint, from Open AI and Entropic and maybe GR or whatever,

If they’re all just using AI with no human in the loop to steer the AI into specific directions, those companies are failed. Like it’s not gonna work for them. So yeah. How do we make sure that the people we work with amplify the AI by doing exceptionally good. Judgment calls.

Nectar: Yeah, no, you’re touching upon how knowledge work is changing, right?

Yeah. Where it’s if you’re doing something that’s really repetitive that an algorithm could do, chances are it will be done by one. But if it’s something that’s, high value, that’s very difficult to scale, that’s where you come in, right?

Edouard: Absolutely.

Nectar: Maybe a final question or two as well is like, what’s the future look like? If you had, we have this conversation again in two years, what does trampoline look like and maybe a little bit of the vision for you, for your company.

Edouard: Sure. So the way I work is, I’m maintaining different potential futures of the company, because I.

Love strategy. And I think a good strategy is being adaptable to the cards you have in your hands. And you don’t. Have control over all the cards that you will get in your hands, like the timing, the geographic situation at your company. A lot of that, changes. So the way we work is we have three potential futures for trampoline.

We’re always thinking of the most ambitious one. But we are also very comfortable with the idea that maybe that won’t be possible. And it’ll still be a very beautiful company. That will bring a lot of value to our customers. So the I won’t go too much into the details there because yeah, like that’s something we try to keep for ourselves and our investors.

But in two years you can, I think we can expect that trampoline will make a significant impact in the service industry and, enterprise sales, for all the pre-sales operations. That’s for sure. And ideally, as I said, like we are trying to maybe become the next fellow, that is something, Gabriel and I very much should you again?

Nectar: Yeah, no. Fascinating. It’s like I don’t blame you for not sharing your strategic plan on it all. That’s fine. Openly with everyone. Man, there’s so much we could talk about maybe to be respectful of your time. If people wanna follow the journey, and gonna can keep tabs on what you’re doing.

What’s the best way.

Edouard: Yeah, absolutely. There are like three ways, to follow us. The first one, is of course, my LinkedIn and trampolines, page on LinkedIn. We don’t use it as much as we should, but that might change. They can also follow, the journey, from the technical standpoint, more on the x.

Gabriel is trying to share, some of his thoughts on the technology that we’re building on X. I encourage them to go there. I’m available also on Instagram where I share a lot of absolutely non work related stuff. Because it’s not just all about work, what we’re doing.

Nectar: Oh, totally has been a fascinating conversation. Thank you.