A (3:03)

That's awesome. I love the stories. We typically kick off the podcast with a great story like that. And you found the two loves of your life, right? Developing coding. And then of course, you found the love of your life, which is now your wife, which is awesome. I really appreciate that. Sylvester, can you explain how Parseur leverages artificial intelligence to automate data extraction and what advantages this offers over traditional methods?

B (3:29)

So, first and foremost, Parser was born in 2016. So at the time, there was not much AI, to be honest, when Parser started, we started it the traditional way. So people would send some documents to us and then we would ask through a visual interface, oh, what data you want from that sample document? And it would draw over it. And we said, okay, understood, you want this data there, then the data here, and we would do it. But it worked great. And still today, it's still a very major feature of our tool that allows to reliably extract data visually. But then AI came a few years ago, as everybody know, and that tremendously changed what we could do. There are many three things that we could not do easily before that, now AI will allow us to do. The first one is that the AI understands what a document is about. They understand the context. So you don't have to visually ask somebody, oh, if you receive an invoice, and the invoice number is exactly at that spot in the document. But if you receive that invoice, it's not there. It's a bit further below. And then before, the people used to have to create what we call templates and show the differences for each types of documents. Now you don't have to do that anymore. With AI, you just say, oh, I want an invoice number. And then you send the full version of the invoice aspects or picture. And the AI is smart enough nowadays to understand that. So it's a massive gain of time for our customers. They don't have to spend minutes or hours even for legal one to configure their Parser account. They just say, oh, I want this data, that data, and off you go. And there was. We were able to go beyond that even. And before we were just extracting data. So it was just, you see an invoice number that's going to be an invoice number. But now with AI, because they understand document, we can go further than that. And you can do translations. For instance, say you receive some leads with a message from your broker in the middle of Miami and you receive some requests in Spanish because there are many, many Spanish speakers there, but you happen not to speak Spanish. You can just ask the AI to translate the message they receive in English if it is not. And that works very well. You can also ask the AI to summarize a document in a few sentences if you have some very large contracts and you want to have a quick overview of it. So read it. What was not even thinkable a few years ago is now possible with AI and just in a few clicks for the user. So that's great.

A (5:50)

Thank you, I appreciate that. And I really thought your tool before the, you know, really the introduction of AI was amazing with that reliably extractive data doing that visually. But with AI, as you mentioned, things, it's just like everything's on steroids now. Your platform has really taken off and of course, you know those complex things like various languages summarizing large documents and not having to create templates just with that, AI is able to parse that information to a higher level. So I appreciate that. Silvestri how does Parseur ensure its platform remains user friendly for clients across diverse industries? And what feedback mechanisms are in place to drive continuous improvement?

B (6:32)

Well, there is some things that we believe strongly in at Parseur is that we hate complexity. We are striving for simplifications for every single thing. So that applies to our user interface, that applies to our processes, that applies how we are organized internally. Every time we see too much complexity, we try to remove it or we try not to have it in the first place. So for the keep our application user friendly, we strive to remove as many settings, Dropbox pages, configuration items as possible from the user journey. And of course we hope that passengers makes it work because otherwise we fail to do what the customers are paying us for. There is always a bell curve. So you start having a very simple app you just launched, you have one feature, everything works well. But then customers comes in and start to ask for more features. And that's great. It means they want to use your tool, they want to expand it. And I think the game here is to try to see how you can increase the surface area of your app without increasing the complexity of using it. Because at some point if you don't do it, you end up in a, in an App that can do everything but is a bit like a gas factory. And just people don't have time for gas factories. They just want something simple that does the job. And I think this, as I said, this simplification mindset is at the heart of everything we do at parcel. We try to we are bootstrapped, we have no investors, we keep our team low, we try to automate as much as possible and try to keep as lean as possible.

A (8:04)

That's awesome. I really love that. I respect a lot for entrepreneurs that bootstrap because that is one of the toughest ways and sometimes some of the slowest and grinding ways to build a company. But I really love your focus on customer, the customer experience and the customer. The goal is to remove that complexity and make it as user friendly as possible. And glad that is your focus. So thank you again. And Sylvester, in your view, how will AI continue to transform business processes over in the coming years and what role do you envision parthur playing in this evolution?

B (8:38)

I think AI will increase productivity and faults by companies. And the analogy I like to make usually is that for me information or data is like money. So if you have $100 bill and you put it under your mattress, it's great. You have $100, it's good for rainy days maybe, but at the end of the day it doesn't serve any purpose. It's not valuable per se. But if somebody pays you $100 for something you did for them, the customers, then you can use that hundred dollars to pay your employees. And then that employee can use that hundred dollars to go for a dinner, a nice dinner at night. And then the restaurant owner can use that hundred dollars to buy the products from the distributors. And distributors can use that money to buy from the farmers, et cetera and et cetera. And then the same hundred dollars becomes so much more valuable. And data is exactly the same. You have some data trapped in your document, on your desk, in your mailbox, on your servers. And it's great you have data, but if you don't do anything with them, it's useless. When the data starts to gain value, when you start to use it and move it from A to B to where it belongs, use it for deriving all the data to make business decisions to grow a company. And AI will increase that flow of data, like the money well spent is increasing the world, the value of the money. And AI is going to do maybe a few orders of magnitude more in the data flow, the speed of the data flow in the coming years. And That's a tech funding shift. I think that's as big of a shift as the Internet or the electricity before or the combustion engine even before that. It's really going to change a lot of things and lock a tremendous amount of wealth not only for companies, but for just the day to day life. And just as for instance for us at Parser, we In the last two years we have processed 10 times more documents than the year before that. So in two years we have increased our volume flow by 10x and this is greatly because of data, because it's easier now. And even if I see that in the future, hopefully document data tracking document will be less and less and there are going to be more system interconnected into each other. So there is going to be less use of Parser. Parser will still be around because there will still be some of that data information trapped in documents. And we hope for Parser to be the main bridge to connect the remaining data in those documents to the rest of the data work.

A (11:15)

Thank you for sharing the analogy as well. You know you talked about AI is going to increase productivity tenfold. And that analogy uses the value of making that $100 and how you spend it. And the values passed on AI as you said, will increase productivity and efficiency of a much higher magnitude. And we can see that obviously through the work you've done with Parser and it's grown significantly over the last couple of years. So I really appreciate that. And Silvestri, it was such a pleasure having you on today and I look forward to speaking with you real soon.

B (11:46)

Yeah, thank you very much, Brian. I love the invitation and it was nice speaking with you today.

A (11:51)

Bye for now.