Navigating GenAI with Incorta

Dive into an eye-opening session featuring Osama Elkady, CEO of Incorta, along with Mohamed El-Prince, Ebrahim Alareqi, and Omar Yousry, as they launched Incorta No limits with an exclusive talk on Incorta X!

Discover how Incorta X is revolutionizing business growth with GenAI, harnessing advanced AI, low-code tools, and seamless integrations to unlock unparalleled data value. Don't miss out on this game-changing insight! Watch the recording now to supercharge your data strategy.

Transcript:

Ladies and gentlemen, esteemed guests, in Corta customers, in Corta partners, welcome to Incorta No limits event. Unlock the analytics future with generative AI here and the vibrant triode.

I am Rodrigo Hadeh, the head of partners and alliances in Encorta for the Middle East, and I will be your moderator for today.

It's an honor and privilege to stand before you today as we embark on this citing journey into the future of data analytics and generative AI.

As we gather here at the dawn of twenty twenty four, we find ourselves at a pivotal moment in history, where technology is advanced is advancing at an unprecedented pace, reshaping industries, and transforming the way we work, live and interact with the world around us.

At the heart of this transformative wave lies the power of AI.

A force that is revolutionizing data analytics and decision making in ways we could have only in our imagination just a few hours, a few years ago.

And today's fast changing world where uncertainty is the only constant the ability to harness the power of data and derive actionable insights has become not just a competitive advantage but a fundamental necessity for long term success.

It's no longer enough to rely on intuition or past experience to guide our decisions.

We must embrace data driven decision making powered by the cutting edge capabilities of generative AI to navigate the complexities of the modern business landscape and unlock new opportunities for growth and innovation.

Throughout the course of this event, we will delve deep into the world of Gen AI, exploring its transformative potential, its real world applications, and the boundless opportunities it presents to our business across industries.

From Corta Innovation, to success stories from our esteemed customers, we will uncover the secrets to unlocking the full potential of data analytics, of AI, of generative AI, empowering the to strive the digital age. But our journey doesn't end here.

As we look ahead to the future, we recognize that with great power comes great responsibility.

That's why we are proud to shine the spotlight to the national data management office framework, the NDMO for national data governance.

By discussing the policies and regulations essential for data classification, data sharing, data privacy, we aim to pave the way for a future where data is not just a valuable asset, but a force for good driving positive change, and fostering trust and transparency in the digital ecosystem.

So let us come together as a community united by our shared vision of a future powered by data and driven by innovation let us embrace the limitless possibilities of Gen AI charting a course towards a brighter tomorrow where ideas transcend boundaries aspirations, no no limits, and together we unlock the analytics of the future.

And now, It is with a great pleasure that we kick off our in Cortano limits event with a deep dive into innovation, led by none other than our visionary CEO, Mr. Usama Ardi, and this session will gain invaluable insights from Usama as, as he takes us behind the curtain of Encorta's innovation journey, highlighting where we stand, where we're headed and how AI and the latest technologies are shaping our product roadmap and our strategic vision.

Osama will be assisted in this session by our engineers, Mr. Hamadil Prince, Mr. Amara Yusri and Mr. Rahim al Araki, Alariki, who will shed light on Encorta X, the latest release of Encorta with its advanced AI capabilities, local tools, and seamless integrations.

So get ready to be inspired and informed as we explore the cutting edge innovations driving the future of data analytics within Corta. Mr. Usama and Tim, the floor is yours.

It gives me great joy to be here after literally thirty two years.

My first job after graduation was here in Riyadh back in nineteen ninety two.

And I was working on some of the first PC, and I was working with, some microsystems, solutions, But today, after thirty two years, I'm in front of you here talking about the quarter, and I'm very, very happy, and thank you all for coming today.

Today, we'll be talking a lot about the latest technology with AI. I am a big believer that innovation has no limits. And the more we empower smart engineers, and the feedback we get from our customers, how can we advance the technology to make them more competitive and allow them to do things that never been possible before. That's what keeps us awake at night and working hard in the morning. So today, as I mentioned, we'll have my colleagues, Muhammad and Omar will be talking about in Corta X, Doctor. Ibrahim will be talking about how AI is empowered within quota and the uniqueness of, us doing this.

In quote, I've been there for ten years.

We have the most important logos in the world using the platform. And depending on this platform. And we were able to add a lot of more than tens of billions of valuation to the companies that we have been working with. We're so proud that we are part of the success of so many logos in front of you here.

And not only international companies like Apple, Starbucks, Netflix, and others, but we're also so proud that we have so many innovative organizations and companies in the Middle East, especially in Saudi Arabia.

I cannot believe every time I come here and I meet with you, the kind of innovation and the feedback we receive from you is really great.

And all the functionality you will see today, someone from people sitting here in this room was their idea why can't you add this? Why don't you try to do that? And that's how we started these, features.

So I might be naming some, but, we are so happy to have many ministries, many big companies, and, many private, entities as well, whether they are in financial services or in other, services as well.

So the DNA of everybody at Encorta is all about business.

We are not just developing technology for the sake of technology.

We are developing technology to help our customers achieve results that will help them and their business, whether to reduce stock out problems in supply chain, increase profitability, improve customer satisfaction, or help in reducing costs.

Every time we think about a new feature or new capabilities, that's in our mind how our customers will be able to utilize these features. And what can we do to be more advanced and to the edge of technology?

That they will always use in quarter, and we will always be part of their success.

That what I say, the legacy way of data pipeline of data architecture Unfortunately, this is the way I've been there for more than thirty years. I used to be at Oracle for twenty years.

I worked with so many customers, large customers that try to implement data warehousing, data platform, and majority of them fail. Either completely failed or did not achieve the results based on how much they spent on these technologies.

The old or the legacy way of doing data pipeline goes through so many steps of shaping the data, doing star schema, doing a lot of modeling in order for them to give data to the business. So by the time you start from hundred percent of data, business is getting less than twenty percent of data most of the time.

Within quarter, we changed that concept.

We are promising our customers that we can aggregate hundred percent of every data around the enterprise.

One percent of the data will also be available to the business users. In the way that it can be usable, accessible and works with great performance.

That's what we have been promising, and this is what many of our customers realized and that's why they've been with us from day one since they start with the platform. People know that this is different.

This will allow me to focus on my business, and instead of getting deep into the problems of how can I get data? To the business, or how can I get to use the data in my business?

Broadcom, this company We started with Broadcom when their revenue was two billion dollars.

Today, Broadcom is more than sixty billion in revenue.

More than six hundred billion in valuation.

Broadcom will always, if you go there, they use only one data platform for the entire organization.

And the COO, the CIO will always say having access to the data in real time, at the right time is what are so competitive.

And that's why they had so many plans to acquire so many companies.

And I was shocked when they told me we can have all these companies to acquire, and we need a data platform so we can integrate all these data into that platform.

Today, we are celebrating with them that they just closed the acquisition of VMware sixty nine dollars.

And Voomware team already moved to encoder two data bytes of data. Of VMware, IOT is now moving to a quarter, cloud, which is a great success.

Alwar Taniya, another example here in the US, in in in Saudi Arabia, very innovative company technology is very important for them.

One thing they told us, we need to make sure that we save the waste. Of when we produce this chicken. We do many times we have to throw that chicken away, and we work with them to improve that I'm sure even the percentage is much much more than fifty percent, but having real time access to data allowed them to build a lot of notification that empowers the whole business how to save, waste on food.

So now to the main topic of this session is about generative AI or gene AI.

A lot of people think it's a buzz word. A lot of people think This is just a high I'm a big believer that GNI will reshape the way that we do a lot of business in the coming ten years.

So the impact on the industry, the impact on the world economy is enormous.

And because we we are a big believer in innovation, we really wanted also to embrace that technology and allow our customers to be able to utilize the latest and the most efficient way of doing this.

So you can see that the most important thing that we really believe about gen AI is improving the productivity of the employees in the organization.

Believe it or not, the productivity can be increased not just by double or ten x, it can be fifty or hundred x.

It can be really fifty to a hundred x productivity increase. You can have actually one person do what ten or fifty or hundred people doing. And the more it is used, the more it will get improved. This is something that the more this technology will advance it will even be more efficient and more productivity to the organization.

And companies who will jump on that technology and think about how they can utilize it today. They will be much more, much more competitive and advancing much faster than their competition.

So before we looked at how can we empower our customers with GEN are? We looked at what are the problems they might face that will Stop them from using that technology.

Data quality is number one. That was a very, very important thing. Because if you have garbage in, it will always be garbage out.

Data governance, I am sure many of you looked at using gen AI, but the companies became very, very, afraid of allowing more and more people around the organization to use it.

Then the trust of the data how can you believe that these numbers are accurate?

And I'm sure this is the most terrifying thing about is the data I'm looking at is accurate or not. And then, coast, I was talking actually to one of the people that I really learning a lot from, Mr. Amal Khastani. He's from Sadeya, and he was explaining to me how much sometimes it costs a lot of, companies to use the data and why they had to stop actually, these solutions. And that happened, by the way, with so many, many companies.

That's why for the last year and a half, we have been working on Encorta X. In quarter x, we really believe that this is a very unique engine that combines the power of analytics, SQL, AI, and ML plus Gen AI.

We really believe this is the only engine and the only platform that combines all these functionality and capability in one platform And this is the only way, the only way to be able to utilize GDI very effective and and very generic, very efficiently.

So when it comes to data quality, The first thing we thought about, how can we help companies with new ways to do data quality That's why we came up with a whole new feature in in Cortacol data studio.

And I can say by the way, that the whole data studio came with idea from our friends at New Libby.

They actually told us, in quarter, it's capable to do this. You can do it this way. We learn from them. And in six months, we were able to develop the whole thing, but we developed that.

Very close, working with them, hand to hand, developing that from site.

And you will see a demo, very impressive demo, and you will also see how that technology also utilizes gene AI.

Every feature you see about every corner in the application now at the platform is empowered with GenAI.

Data governance also, we came up with a whole new concept called Global Smith Claire, We improved on the semantic layer that in court already has, and we built that back in twenty fifteen.

Now, so many companies are trying to think about how can they enable semantically in their platforms to allow The security is needed for GenAI to be empowered there. And then the trust, you see that in quarter is the only platform, and we know that. And we have no competition in the last ten years. That any other platform can provide hundred percent of data in real time or near real time.

It's impossible to think that you can get sixty different ERP systems, put them together and provide data to business user in nearly we have customers, big, big oil companies in the US doing this. Most of our customers They have multiple ERP systems with tens of thousands of tables.

Very complex systems, and they were able to deliver that in near real time to their customers.

The full fidelity, we talked about hundred percent of the data, not just subset of the data. That's why I'm sorry to tell you that the whole data warehousing solution that been implemented will never work for GenIari. And customers who have been invested a lot of data warehousing. They really need to rethink and re evaluate the whole system to be able to empower Geni because We know that data our houses does not have all the data, and it has a lot of aggregated numbers that will never work for Geni.

And then this is now the thing that we have developed the first ever co pilot, what we call, in quarter co pilot, that connects to multiple LLMs, to be able to improve the efficiency, the speed, and the cost for our customers. We actually now filing for patent for this. We're the only company in the world. That is allowing this functionality, and we'll talk about it in details.

Encorta co pilot is the heart of Encorta X. This is the engine that we work on very hard. We have the most innovative, the smartest engineers we have the work on this technology together with a lot of partnership with so many companies to make sure that we can do something that is very unique very unique, but very innovative. And this is when I say innovation has no limits. This is an example of that one. In quote, a co pilot is empowering every part of enforcer. And you'll see how.

In quote, a co pilot, actually, when you use encoder today, encoder X, and you connect to encoder co pilot, you are not just using open AI.

From Microsoft. You are not just using Jim and I from Google. You are not just using Victoria or LLM.

Or Maestraal.

You're actually using all of them.

So we heard a lot about the KLLM and we heard that companies they are doing the same question with multiple of these systems.

In quota copilot is different.

In quota copilot is the only engine that takes one question from the user.

Do planning for that question separate the jobs into multiple tasks.

And then uses multiple LMS to answer one question.

It looks for each section and see which one is the most efficient from speed, from cost, from accuracy.

And depending on that, it goes to that model to answer that piece of the equation.

And then it has the dependencies between each of these models, combines all that result and then send back to the customer.

That can reduce the cost of using GNI by ninety percent.

And that's exactly why we develop open co pilot. Because price and cost is the most obstacle for customers to use in AI.

At the same time, it is improving the performance and also improving the accuracy.

Not just with the available, LLMs that we talked about, but with a lot of private LLMs.

Customers can actually plug their own private LMM to open Copilot.

Customers can extend open copilot and develop it for their own usage as well within the organization.

I really believe in quote to open copilot would be as important as in quote x for so many of our customers.

They can build their own solution, their own applications on top of in Corta Copilot. And actually, I'm very happy to share that we already make it in Corta Copilot as an open source, and we would love for our customers for smart engineers around the world to start contributing to encoder Copilot, because it's not just encoder. We really believe that customers need to develop their own applications using encoder Copilot as well.

That's why, by the way, we have been working very closely with Microsoft since they start talking about open AI. We got very early access Microsoft is a big investors of encorcester, and that's why we have access to some of their, technology with a special price as well.

Gemini, I've been talking to Thomas Korean since last year, how Gemini is not even comparable to Open AI, with the latest release of Gemini one point five, we're really very happy that Gemini now is very, very close to CHEBT for all.

We are so happy that we contributed a lot and we had continuous meetings with the with the Gemini team engineers, with engineers, because we want our customers to have multiple options. That is the only way to reduce price. If chargeability is the only option there, the prices are very tough, but having multiple options is what will make it great.

And then, Viktara, this is very close to us. Viktara is very great innovation company focused on text, and focus on avoiding hallucination, making sure that the security is there.

I hope someone can help me. How can I run the video here? Do you guys know?

Yeah. It will come. So this is the announcement that we have been talking about. On LinkedIn.

Can somebody from the technical team?

Get back to the slides, please.

Sorry about that.

Can you please run it from your side?

Can you run the video?

I have some very exciting news to share with you.

Viktara is proud to announce our partnership with Encorten.

Today, we are unveiling our collaboration to provide the most complete GENI platform for every enterprise.

Viktara specializes in unstructured data, like documents, while Encorta excels at structured data.

Together, we will be able to answer any question spanning every record and every documents in the enterprise.

This is a significant milestone for both of our companies, and we can't wait to see what the future holds. Thank you very much.

So I really, really appreciate the team at Victoria very smart engineers, and we have been working very closely with them to make sure that we bring all that functionality to the Encorta platform. Because adding unstructured data to encoder platform will make again the dream of gene AI is more complete, because we cannot just depend on structure data only. Gene AI, we really need to add a lot of capability about unstructured data as well. So that's a huge milestone to us and having our engineers working with Tara engineers was a great example of great collaboration.

And the same thing, we will continue work with more or more innovation innovative companies to bring all of that color to the platform.

This is the in quarter We call it the most advanced operational lake house.

If you know that we invented lake house back in twenty sixteen, I was talking to some engineers today. They would say, we thought this is the normal. We don't know why people now make it a big deal. Our customers been enjoying lake house since twenty sixteen. But now with GNII, you will learn very soon about Encorta X. You will learn about the global semantic layer. You will learn about the lake house, and you will see how many capabilities we did, not just for the AI for many of the regulation requirement in Saudi Arabia around MDMO.

We understand by September, there'll be a huge fine for companies who have any accident about security leakage, and that's why you are adding more and more functionality to the platform to make sure that we support our customers going through this journey.

So in Costa Cloud in twenty twenty three, we added one hundred percent. We doubled our customers. When in Corta Cloud, worldwide, we opened in Corta Cloud in Japan, in Singapore, in Europe, in Qatar, but also we are so happy that we now have in Corta Cloud available in Saudi Arabia.

That's a dream we have been waiting for, and we are really, really excited that our customers can continue to enjoy in quarter on prem as much as they want, and they can even enjoy gen AI with private LLMs, but One, I also utilize the cloud with more machines, automation, more scalability. They can also do that. We will always have all the options to our customers.

So with that, thank you very much.

I wanna thank all of you who are coming today, and I cannot wait for you to see all the exciting new features are coming. Thank you very much.

As Gerta was built from day one for empowering business user, for self-service.

Can you hear me? For self-service, for business users, okay? So, for for that reason, we're trying to, we're trying to reduce the time for business user to reach the answers for business questions.

So I I remember, first couple of meetings with, Magic, our, our GM He asked me a a business question, and I told him, give me three days to answer this question.

Three with Encota, within Corto, we found some, we have we have some customers' testament that they can build reports from up to three days from scratch without previous previous knowledge of encode. Okay. But this is not enough for Osama.

Or someone always wants, to break the, the triangle of coast performance and, and, and quality.

Okay. So how to reduce these three days for a nontechnical business user and answer a business question from his CEO in a few minutes. Okay.

So that's why we're introducing Encorta Open Co pilot.

Now, within Corta X, we're talking about four different pillars, augmented analytics, machine learning, advanced modeling and query accelerator.

I'll try to run through the slides quickly so that we have, time for the demos. For documented insights, you ask a question in a in a natural language, pick, it it picks for you. The best visualization it summarizes the the answer, and it shows, related insights that have been built before inside in Corta. So that you don't have to rebuild these in sites again and dashboards.

You can ask it in any language. This is an example in in Arabic, and it it understands, almost any language.

Next is not, we're not stopping here where, where we, in quote, a copilot helps you to build on a single insight. But also a whole dashboard. You can generate a whole dashboard by choosing your business schema business view and give, encoder open co pilot a topic or a set of questions, and it will generate for you a whole dashboard starting from a PowerPoint template style with a with a title, with a introduction with some insights and, their summaries and also an auto generated conclusion with some, with some suggestions of what to do next to improve your, your sales and so on.

Next we're also introducing, data storytelling, which also generates for you a whole story on your data and gives you a perspective and- perspective insights for you.

Next is in quote, a notebook for business users. This is a newly introduced feature that was introduced in twenty twenty four, the pound zero.

That will enable business users to use notebook. So advanced business users who can use notebook and write Python, they can now use it for analytics from, in court, a catalog page. Okay?

And not only that. Some business users are are not that technical to write, Python. So we also introduced in Corta Open Copile in in business notebook so that you can ask a question and it will generate the code for you. It can also debug the code for you. You wrote something that gives you error. You can ask in quote a co pilot for the error, and it will give you the the the correction of that error of that code.

Next is the data studio, we're introducing the data studio for for, for data modeling and, in in the next level in the data stack. And this is for, for for schema managers who are building, MVs to to build that a whole pipeline of data of data pipeline and processing this needs an advanced user who can write a complex MV, for example, Okay. And now with Encorta, that studio, you can build this pipeline visually with a very easy steps of of pipeline.

Okay. And also for, to to enhance the performance and the productivity of of of this user we introduced also in quota Open Copilot to generate you, code to, to, to add steps and stages in the, in the pipeline.

Next, we're we're gonna talk about the global cementicleer.

And as Osama mentioned, this is a crucial crucial thing for, for co pilot, for origin AI, for accuracy, for data governance, for data catalog, for the data classification. Without all this, you you it's it's impossible to use is an AI on top of your debt.

Okay. So on top of, the global cementicleer, we're introduced, you will see in the in the next sessions, we'll talk more about the data governance catalog, the, quality and classification, so I'll go through. Quickly through this, and this allows us to, to run and quote a DDM on on Delta, format tables. This allows us to run remote tables on these Delta tables and allows us to execute, execute virtual queries on top of, Oracle my sequel, and so on.

This is an example of, the data catalog screen I'll just talk about the related terms. This will enhance the co pilot results, tags that will enhance the searchability, and unfiltering domains will, enhance building data, data applications, and data classifications, for classifying your, PII data and so on.

And now with, Lakehouse, with Lakehouse, we'll we're introducing deletes. This is, a very old task that we need to inter introduce deletes on your data, moving window, remote table and delta sharing.

Next, we'll talk about the marketplaces. We're in we have introduced multiple marketplaces we're, that and the concept behind the marketplaces is to use the power of the community and, partners, and our customers to build more content and share this content with other, customers so that they can get use of that without having to build this from scratch.

We have partners who build, data applications. We have new visualizations. We have customers who shared and, and contributed to our components in our marketplace. We've introduced the connectors marketplace so that you can build your own connector. You can get who are built who's who's built by other customers or other, partners and so on. It also it also allows for easy upgrade for such component such, connectors without having to upgrade your encoder or go through any cycles. With just a few clicks, you can upgrade any component or connector.

Okay. Last, we're we're planning to introduce ML and AI applications in in Mark Place too.

Now, this is, a list of our of our partners and integrations.

We're integrating in in many aspects like data acquisition, with C Data and Faldero, external compute with data pricks, data governance, cholibra, and informatica, machine learning data IQ and sparkflows, visualization, Tableau power bi, looker, and so on. And definitely sorry.

And definitely augmented analytics with Viktara and superior.

Okay. Now with the demos, Omar Usery will join us in in the demos. I'll start with the augmented analytics.

Okay. This is a demo of in of, in court, co pilot to generate an insight.

Sorry? Hamat.

Hello?

Can you please play the video?

Where the demos also will be available on YouTube as well. And I'm sorry.

Okay. Thank you.

Yeah. Okay. Can you please play the video?

Yes. Thank you. Okay. This is an example of, a question top, ten products.

Okay.

Okay. The example, I'll, short questions. I'll show it as Ella. It sorts el el data. It shows, multiple capabilities, el sorting, filtering, conditional formatting, you can add, Ted dot edl, l, l, l insights that is generated insights to your own dashboard.

So you can start building the dashboard from, dogma, from the Encota Copilot two.

In a be rural, the planning, or behind the scene, actor actor.

The dashboard with a demo user.

Quite a lot of time. I don't know.

Can we play this with you too?

Okay. The example, Taney, is to build a whole dashboard from inside the Encota, also using the Encota Copilot, open copilot, as Usama mentioned, Henneth had a telotopic, had a telotopic out a telotet of questions and it will take this into consideration, how to generate, how to initialize, how to, how to, how to, how to, how to, how to deal, insights with some summaries, the conclusion, the suggestion, what, terminal a, what to do next to enhance your sales.

Please.

Okay. Now with Amari Street will show the notebook.

And machine learning.

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Type, Anna Omariosri, Tawanta, you know, in Pepsi, Mr. Mustide, Vanna, Tarpino Engineer, cloud engineer our engineering manager.

For an accidental cloud.

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Algenie O'Iandra, with a Elena Enernahan employee, Allaha, and Notebooks for, to introduce in Korda Kupilot, four notebooks.

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For the next segment, maybe we have built notebooks, but we introduced something better.

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Okay. Like, moving on, then introduce powerful tool, manal, manal notebooks. They don't know how to add ill users.

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Okay.

AHA use cases, two tables.

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Where Nishil, full names, like in law, is in Hassan, how will model aq?

For to introduce and then our data in the default.

Is out column three, and add three recipes and add column concat, a lost name or first name for Haitleo and then a full name as an introduced Durati.

Three possible names were golden names.

Of the names the antenna table grid, or call grid, the whole, cleansed country. Well, I can, when applied, when create receiptic leader, Zaima Otenko, select Masa and column.

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So, like an, a there, multiple versions multiple selections, data flow, other than a store, multiple versions, above the IE section, when I can fill video. We introduced, will be replaced, recipe, code. They in data quality, we'll code the enhancements and I, remove columns where a duplicated result for Mugarad was hacked and islam.

I will data studio. We don't know why support.

As a training model or training set other than machine learning.

There are a lot more to this, there are hundreds of recipes, people can use, Mahadwab, seed, Gidhudan, later in Hawaii, he saved millions of complex queries data studio, our data flow way.

To improve performance, but I'll I'll I'll sequel into Facebook for the time my name is Ibrahim.

I was born and raised in and then I studied my undergrad in Malaysia and in US, my master's in US, and also my PhD in the United States of America.

But, can, but they eliminate Windows three point one. Madrid three point one.

Okay. About in three point one, in my My love for computer grew, and I started coding. And here I am today, joining in Corta as a principal machine learning engineer prior to I was working with the autonomous drive function at Vovo Cars.

How do I translate this?

My focus was in scalable science, systems.

From data, where we hear a lot about AI and GenAI.

And maybe I will walk you a little bit through the landscape of AI.

Deep learning, data science, Genai, Ashil Isayar? What's going on? And I'll walk you through this. Lacking up a recom one chart.

The AE machine learning system, AE machine learning system with the talent terminal, you have to go through these steps, data pipelining, machine learning pipelining, and the software code that you're trying to build.

More of them are machine learning engineers.

A data scientist with data engineers. Yeah.

I mean, walk to home, fuck up for data exploration and validation for wrangling the data and cleaning the data Four, making sure that the data is profiled ready for the machine learning model to be consumed.

A machine learning model, with Incorta.

In Corta abstract all of this for you.

So there were index machine learning engineers, there were index data engineers, there were index data scientists, but then my more of them walked You work for data preparation and data wrangling, bringing the data from the source to g b data from the sources within ERP systems, In Cortel literally make this abstracted from you.

Kalihah wab ala kunluffy business layer, Culofy Semantic layer, and it's easy now for the machine learning engineers.

Building the model, they have now more time instead of twenty to thirty percent of their time. Now they have more time to do model engineering, model evaluation, and packaging their model to be embedded in the code that will be serve users.

Now, how is in court doing this? We will come to this part, but and you've already seen enough enough demos, but they'll shift to him, and he won't let me tell you about machine learning and deep learning, whom are literally in this box.

They think about this little box that can use Sue in order to deploy a machine learning engineer our machine learning system to serve your need, your organization needs you need the data collection. You need the configuration, the automation, the feature engineering, the process management, the model analysis, serving infrastructure, It's a lot of work.

And in Corta, abstract most of these for you so that your engineers can focus on building the model that serves your AI and ML needs. And this is for all levels at the organization. If you look at the AI lens, and here is AndrewNG, a well known figure in San Mado Phil machine learning. When machine learning is named, Andrew Angie usually is one of the main names that come out Indro Angie, if you don't know him, who again, what happened in NASA, Libeta or Google Brain, or Google AI, who will live at thechedelic, Baidu, ai lua, Google of China. He is a professor, few miles, been in Corta office for Foster City, for, for Palo Alto at Stanford University.

I personally worked with AndrewNG, developing the deep learning, course for Sarah, helping them with the questions. AndrewNG be that in the ai landscape when chief in the supervised learning, which is part of machine in general and AI will grow up in the next from today to the next three years, it's gonna grow up actually more, more than double the size.

In generative AI, which is also using supervised learning in some cases or semi structured supervised learning is gonna grow as you can see more pro probably triple the size.

Similar thing with supervised learning and reinforcement learning. And the good thing is, is already ahead of all the competitors in in implementing and embedding generative AI in our in our core product in every single layer that serves every single customer that serves every single persona in our moving forward, and this is how JII look during the history.

We used to view the old lab systems through dashboards.

Kunnebastalamil View. That's how much insight we get. Bad in, companies like Tableau and whatnot started to do, to filter, edit the dashboard.

Ashankun customer, a cool viewer, and he's able to see the things that he want to see, but I didn't get in that you discover from the dashboard. And this is where Lucer and some other companies were actually looking or working on. And within Cortenel, you can really discover your data no matter.

Come corporate data, big, small, we we scale.

And also, using natural language.

Doesn't really matter. But but mostly here in English, you will be able to ask a question and the insights coming for for you you discover from everywhere, whether it's at the ingestion, whether it's at the exploration, or while you're developing the insight or the dashboard.

So literally, we are striving and we are a company, probably the only company that is truly a self-service company when it comes to the user and the business user.

So And I'm with either CEO or CIO or someone, a director of something at your company, and you're looking at with Fekular case and animal strategy, an AI strategy, Fishelicander.

Really? If you wanna, have a presentation carefy, care for the data accessible.

We should nestle if I have infinite data. Number two, think about The staffeed, aqbar, Adramoonke, Middle gin ai.

I'll advance analytics and reporting, Mr. Damil gin ai.

The data is getting bigger and bigger and bigger.

You cannot you cannot scale the team. You have to scale the technology. And you know the data is gonna get bigger.

And to manage good tools, good software, good systems that will help you scale as the business grow. And lastly, With in Corta, you will be able to integrate with a lot of other AI tools. In Corta pride itself, and an open platform at every single layer of the data. Every single layer of the journey of the data.

After you ingest data in Corta, you're open to actually use it with any other company, meaning in Corta integrates as how much, show you in the beginning, my other AI platforms, They they tie queues, they sparks flow, they all other, you know, with Google, Microsoft. It's easy to integrate.

Also, we have our own data, but since we are an open platform, in the, you are not locked in the in the quarter. To stack them in Kota as your lake house. There's no fee like other AI platform that you're interested in into Mokiyani team in Cortel will help you easily and seamlessly integrate with it.