 Hello, my name is Alexandra Emanuele. I'm the digital content editor of Global Food Safety Resource. I'm here in Seattle at the GFSI conference. And today I'm speaking with Nicos Manuseles of Agrino. Nicos, thank you for speaking with us today. Thank you for the invitation. Of course. Tell me a bit about the work you do with your software platform, Foodacai, in extracting data from the supply chain. So what we do is that we scan food safety data globally from official sources and authorities around the world. Data that's coming in different languages, not only in English. So we scan sources that have information in Chinese, Japanese, Czech, French, Greek, and so it goes. And what we do with the data is that then we translate it into English. We harmonize it. We annotate it with the right taxonomies, as we say. We put the right product codes, company codes, all the codes that you need to generate the insights. And we deliver insights as a service to the food industry. OK. And how can that data be used in real world scenarios, predicting food fraud? As a matter of fact, we had this discussion yesterday with one of the large retailers in the UK. They're trying to assess the risk of working with specific countries or with specific suppliers. They work with more than 3,000 or even more than 3,000 suppliers around the world. And what they're looking at is whether they can evaluate what is found in the data about the countries, the emerging risks, the hazards that seem to be trending in ingredients or raw materials or products, and take decisions of where they should invest their testing money. So that's one of the testing scenarios that makes sense. Absolutely. And is it used in predicting food fraud or? It is. This is where we put a little bit of I in place, together with other data sources. So we try to see if we can find correlations between product recalls and trends in product recalls for specific incidents. And country indicators, like corruption indicators or spikes in the prices. And if we find and indicate a connection and correlation that is statistically significant, then we point this out to the decision-maker. Fascinating. I mean, we were talking earlier about how your data has some really interesting things to say about milk production in Europe, for example. Is that an insight you can elaborate a little bit more on? So what we do is that every three months or so, we take a deep dive into specific sectors and data concerning these sectors. And the data sector is such an example. And we try to look at product categories like milk, cheese, butter, yogurt, or other relevant products and see for these product categories and the critical ingredients that are being used in products that are going together, like, for example, fruits in frozen yogurt. What are the trends? What are the trends of the past 10 years? Which were the trends last year? What can we see as an emerging issue or number of issues that come? And then again, highlight this as a potential issue that someone has to investigate further. And after this is done, what typically happens is that the people in the quality and safety teams, they sit with our team and we start digging deeper into what does this mean, why does this happen, what's happening with specific suppliers. Yeah, I love that you could really drill into the data and find out what the particular bacterium is that's causing issues in, say, milk production or that it's a bacterium that's causing so many recalls. I think it's a journey to the data. It starts by the highlights, some of the highlights that we see. And then this creates an opportunity to start digging deeper, drilling down into the data to find what is relevant to really important decisions that people have to take. Where should I invest my lab testing money? How can I assess before I order while I'm expecting an order from another country? What will happen in a lot that's arriving with products from a supplier? And so many other diseases that can support. I'm sure that your data has made a real impact in these scenarios. Is there any stories or one story in particular that you might want to share with us in regards to that? My favorite story is from a confectionary products manufacturer. We were demonstrating the platform to them and we were testing their name into the global incidents that were associated. They were OK. We know this. We are monitoring what is happening with our products. And suddenly, a border rejection from China came up that they were not aware of. And they took a note immediately and said, OK, we didn't know about this. That's interesting. Fascinating. Well, thank you so much for sharing this with us. And I look forward to seeing what the data reveals and the stories it tells in the future. And the stories it tells. Thank you.