 We want to look into matters of technology, how things are evolving. We have a young person here who has invented a way in solving problems to most of the industries in terms of data and that is where we want to begin and to understand what they do and how it is helping the industries out there. Good evening sir. So, maybe you could tell us what you do for a living and so much speak about this technology. Okay, my name is Lassiti Pesi. I'm still a student. I was saying computer science, mathematics, much. Thank you, sir. Okay. Unfortunately, I'm still not working anywhere because of the studies and everything but we have come up with a very good transit, a startup that will help the industry out there. All right. Would I be wrong if I say it's a Sahara data tech company? You are still in school. You have come up with an idea that is helping businesses out here and maybe probably tajipatomise idea safari kum. How do you feel that happens? Hey, that one will be the peak of everything that I'm working right now. You know, generally when I interviewed a pandio, we were speaking about Akari and we were saying young people sometimes go out there for internship or attachments, they get employed or they are given small, small stupends and they feel like if I'm running and I go shule, why do I need to go back? Maybe no one wants to go to school or no one likes school for that case. So, in any case, tajipatomise na say to big companies, how would that make you feel? Would you go back to school? Okay. On a personal note, I consider myself a professional. So, going back to school will be a must. Okay. So, maybe you can begin with what is it that this technology does? Okay. Datasans. I'll talk about datasans. I first tried to find datasans for the crowd that understand. Datasans is a field that incorporates mathematics, programming and content knowledge. In order to derive logic from data. Datasans. So, basically, there's a certain meaning of data, other than what we call kusaba, but when you do a data name, Bandos and everything. Bandos is the internet. Okay. That is the information that can be stored. Okay. Including common numbers, which is medical and everything. If it can be stored, that's already data. All right. So, in what you do, I see there's something to do with data analytics and data science consulting services. What are these? Okay. Data analysis. Okay. This is understanding data. Trying to understand what is going on inside the data that Tunaika, Mali, Kamaqa cloud, Kamaqa database and their likes. So, these data we develop what we call, we develop models, machine learning models. This machine learning models and try to understand past data. Try to get patterns in those datas. And then when they get those patterns, you'll be able now to make decision, future decision based on the previous data that you have there. So, if a company, they have given you certain data, you have processed it and maybe you have now giving it a meaning. What is this exactly that you have helped that company to do? Okay. Let me give you an example. Let me give you a very good illustration. Let's say we have companies. We have, what's the name, a company producing one certain product. Let's say any product. Now, in this product, they have been there for a long time. So, they must have data that they have been. So, we understand the trends for easy data, that is what we call a machine learning model. This machine learning is like using AI. This machine has been learning these patterns for a long time. It has been learning what is the name, what is the color, what is the shape of the product, more than the other one. So, what is the name of the product, what is the shape, when I produce more of these colors or when I produce more of these shapes, people will buy more of my product. You know, I will forgive you because you are speaking from the knowledge point. So, give an example of a company called Joe, which is Jewish. Here in Machungwa, we have a company called Pineapple. So, how do you define the market? Is your technology helping them to know what is going on in Pineapple? Oh, the brands, okay. Now, okay, that's a good example you have given. Okay, for Jews, like we have, we have, we have producer, we have Jews, we have pineapple and orange and they like, you know, one of the people they like pineapple mostly. All the people like this other, like same, come on, you saw that in Christ. Come on, I'm like, I'm like Christ. I'm a stony. So, you see, now, kwelewa, kwelewa kupata, like what is going on, this kweyo data, you have been there in business. You have all that data stored in the database and the cloud. Okay, now, kwelewa kupata, kwelewa kupata, kwelewa kupata, kwelewa kupata, kwelewa kupata, kwelewa kupata, kwelewa kupata, this kind of Jews come from Pineapple. Adel's one of these type of Jews. So, kwelewa kupata, kwelewa kupata, kwelewa kupata, kwelewa kupata, kwelewa kupata, these colors, they more need to be designed to be put in stock. We need to choose the way it's sold in a black or white color. So, jezznaka m returning to kwelewa. They just möglich Kakiaวapu. We just give you that business intelligence of understanding the prospective customers. We'll help you to understand the prospective customers. Alright, so even if you want to me understand, a company is dealing with the Jews and they want to brand them into the Jews. So they want to be in January to lose this amount. February to Kauzaivi, March to Kauzaivi. And those amounts, because brands, for example, brands, brand flanis, pineapple. So you just create your curve and out the graph? Yes, so you just create your pattern, like when you are in a business, you will be able to accuse the Jews. So if you want to go to Kulzaivi, you can do it. Yes, that's what I'm saying. For example, I have a kwa msimonya, I have a matisho. I have a matisho in different colors. You can do it in a matisho plane, you can do it in a setting thing. You can do it in a matisho plane or in a matisho plane. You can do it in a matisho plane, you can encourage words. You can do it in a matisho plane in a matisho plane, you can promote football players. So that's the classification of our joint, those customers. Right. So whether you will layer or layer, which color and the likes. And from now the information you have is from a company or an individual and this is how you should be operating. Exactly, that's how it's called, consulting. We mentioned logistics. logistics and… How are you helping a company to… OK, for logistics, now, Sedia will improve their services through improving the time of delivery and everything and then to reduce trust. Sedia can understand how they can reduce the cost.確ifia wiha uhu value, moneginu peruu, una initiativeci, pili na roats porka flexi Uwa ka siwa kimi uf Unwo tukana hiu ruti ya na kuru, tukana this other one from piti ya na ruk and the likes. Now, wuki yangali ya past data, wuki piti ya, umu kwa kwa, you've been storing all that data in Samoa. Umu kwa kwa kwa kwa kwa wiki pili kave to kericho, but you've been storing all that data in Samoa. But you've been storing all that data in Samoa. Umu kwa kwa wiki piti ya root mostly, umu kwa piti ya root and then some other time, some other time they are back there, umu kwa wiki tumi ya pii ruti ingine. Wuki yangali ya kwa hiu ruti ingine, umu kwa kwi tumi ya certain amount of fuel, na ingini umu kwa kwi tumi ya certain amount of fuel, kwa tumi ya certain amount of time and then, ifu, ifu, kwa hiu ruti zote. So, she talks idea kona which one is more efficient, which one is going to be less costly to take to your logistic company. So, you're not only helping in the logistic plans, but also you're helping the company to understand how much they can spend on a certain means or the other. Okay, so serving their problems of usage. All right. So, what companies have you worked so far with or which companies do you feel like you do want to work with them that it's a start-up? Okay, I don't have to go up there in the end. First of all, we have to find a work in the government because governments, for example, right now, do you know that this same thing can be used for fraud detection in banks, in, like, isumavetu kwa perastatos and the likes? Wait. So, gavaika pia na pesa kwa a certain ministry or a certain perastatos, pese tumi kewibaya, he technology in his details? That analysis. Tuna analizio. Tuna iliwa kwa. These ekitu nilifoniki, epesi liende, epesi likwa distributivi naivi naivi. Kwa bank, the same. Kwa clienta. Kama, kama mitu naivi wa account, misi ekitu naivi wa account hijaki, aduana hijak account liya kwa. Itu kama isu asitu naiva kwa liya. Nandini kwa mabangu za, ksibi and the likes. Mekwa kifes such challenges. So, we are trying to solve that problem in this site, your fraud detection and the likes. Data analysis is in the final. Because you're just analysing the data kwa kiwek kwa kiwek. Tumi apatansu naopatansu nilia. So, maybe as we wind up, ni challenges gani, amani limitations, gani ziko kwa hi take? Kwa aia. First of all, kwa explainia mitu eleki in a intel, it becomes a challenge. Because, watu ekisana, badawai kwa technology intel. Na pami apamis tago. Badawai kunda ni a take. Una. Laki isasu na jwa kelektu nipolepole. Una chikulia watu tuno eleziya polepolepole. So, the most challenging main phase ni just the crowd to understand watu data analysis entails watu data science is all about. All right. There's an organisation. It's called Kenya Association of Manufacturers and they deal with the manufacturers and they help them. The support young innovators when it comes to technology and it can help manufacturers and companies. I'm hoping you will be looking forward to working with them or maybe other people who will support your invention, otherwise asante sana kwa kuja. I'm sure someone has learned something out there, especially when it comes to logistics and the solving certain problems and knowing that corruption can either detect per se ikingia kwa una chikulia watu maybe this can help us to know even the hijacking. They back home. Thank you so much for staying with us. He has been my guest. La City Pacy is a tech guru as you have had. My name is Dereva Hilewi. I will be seeing you again tomorrow. Same time, same place. Until then, have yourself a very good night and enjoy the rest of your programming. Good night.