Yandex Blog

Say “Privet” to Alice, Yandex’s Intelligent Assistant

Today we are excited to officially introduce the world to a new AI assistant, Alice. Alice speaks excellent Russian and integrates multiple services in one centralized tool.  Built to authentically interact with people as they go about their day, Alice understands users’ natural language requests and provides contextually relevant answers. 

Alice helps users to easily and efficiently navigate their lives in a variety of ways by planning routes to destinations, providing weather forecasts, and sharing the latest news, among a number of other useful tasks. In addition to providing users with a wealth of online information through Yandex search, Alice integrates our services such as Weather, News, Maps, Transport, and Music, offering a centralized and fluid experience to assist users with their day.  For instance, to find a restaurant nearby, one can simply ask for “a dinner place nearby” - and to get directions to the suggested restaurant, they can add, “how do I walk over there?”

In developing Alice, we utilized our knowledge of the more than 50 million monthly users who interact with Yandex services.  Coupling our machine learning capabilities and 20 years of experience with Russian users and Russian language, we started experimenting with a personal assistant to specifically serve the needs of Russian users. We released a beta version of Alice in May 2017 to further test our ideas and understand how users could benefit from using Alicе.  Recently, we added a neural network based “chit-chat” engine to Alice that allows users to have free-flowing conversations about anything. It is a unique feature that our users find surprisingly delightful and different from other major voice assistants.  

Using web-scale datasets and deep neural networks, we trained Alice to listen, understand, and speak to users using natural language.  As a result, it provides an authentic and human-like personal assistant experience.  For instance, when a user asks Alice, “What’s the weather in Moscow?” and then follows with a question in slang asking, “And what about Peter?” Alice understands the intent and provides the weather forecast for St. Petersburg. 

Alice leverages speech recognition and synthesis capabilities of SpeechKit, Yandex’s world-class toolkit that is used across many of our products, from Navigator to Music. Speech recognition is especially challenging for the Russian language due to its grammatical and morphological complexities.   According to word error rate (WER) measurements, SpeechKit provides world-best accuracy for spoken Russian recognition, enabling Alice to understand speech with a near human-level accuracy.  

SpeechKit’s text-to-speech (TTS) technologies utilize multiple machine learning methods, such as neural networks and gradient boosting, to synthesize responses that sound natural and authentic. In addition to authentic responses, users who are fans of Spike Jonze’ film Her will recognize that Alice’s voice is derived from Tatyana Shitova, the recording actress who dubbed the voice of Samantha in the Russian language version. 

Smartphone users can now use Alice in the Yandex search application for iOS and Android and in a beta version of Yandex Assistant for Windows.  Alice will also soon be integrated into Yandex.Browser, followed by other Yandex products. Thanks to users for choosing our intelligent assistant, and even helping us name it.   Welcome to the world, Alice – we look forward to talking to you!

Yandex Turns 20

On September 23, 1997, the Yandex search engine was unveiled at the SofTool Exhibition. Soon after launching Yandex, our cofounders Ilya Segalovich and Arkady Volozh saw an opportunity to provide users with more than a search engine. “The user’s journey doesn’t end with search. We understood that very early on,” said Yandex CEO Arkady Volozh. In our first five years, Yandex added a number of services, including email, news and image search, eCommerce, and online payments. We also launched Yandex.Direct, the Russian Internet’s first contextual ad placement service for businesses who wanted to advertise on Yandex.

In addition to helping users find the things they want online, by the mid-2000s, Yandex began supporting users’ offline navigation needs as well. In 2004, we launched Yandex.Maps and two years later, we began displaying traffic information for users in Moscow, helping them better traverse the city’s notoriously congested streets. “Over the years, the internet has not only grown in size, it has also penetrated into all spheres of life,” said Dmitry Ivanov, Head of Discovery Products, who joined Yandex in 2003. “At Yandex, we’ve kept pace with this growth, expanding our services across many areas of users’ lives.”

Longtime Yandex employee and IR Director, Katya Zhukova recalls one of her favorite memories from the early days, “We constantly thought about our users and joked that every new product had to be designed simply enough that anyone, including Arkady Volozh’s mother, could use it. We called it 'Volozh’s Mothers Test.'" A combination of top tech talent, as well as best-in-class technology, have enabled Yandex to make products that pass the test.

In 2007, we opened the Yandex School of Data Analysis, a free Master’s-level program in Computer Science and Data Analysis. Many graduates have gone on to work at Yandex and grow our machine learning technologies. “I truly believe that it is not just the deep expertise in machine learning, AI, neural networks and the brightest, most talented employees, but also this user-oriented approach that has brought us to where we are today,” says Katya.  

By 2011, the world began to take notice. We held our initial public offering on the NASDAQ stock market. We also launched Yandex.Taxi, our market-leading on-demand transportation service.

Grigory Bakunov who leads all health care related initiatives for Yandex and has been with Yandex for over 15 years describes the maturation of Yandex over time as “we started as a typical startup: do it faster, fail, do it again but another way.  But year by year we grew a full stack of new technologies and systems. Some of them are now state of the art and at the bleeding edge of computer science.”

In the past five years we have focused on personalizing our products and services to the increasingly mobile needs of users and businesses. 2017 has been a breakout year for Yandex. Our Yandex.Taxi business merged with Uber to form a new company that will serve 127 cities across six countries. We announced a joint venture with Sberbank to further develop our Yandex.Market platform. We also open-sourced a machine learning library based on gradient boosting called CatBoost. 2017 also ushered in greater choice for millions of Russian Android users who can now select their preferred search engine on their mobile devices.


“As I reflect back on the past 20 years, I want to thank the entire team at Yandex, and our users, for joining us on this journey,” said Yandex CEO, Arkady Volozh. “We remain committed to our mission helping users better navigate the online and offline world. Wherever the destination –  online, offline, or something new altogether – users can trust Yandex to take them there.”

One model is better than two. Yandex.Translate launches a hybrid machine translation system

Today Yandex.Translate launched a hybrid machine translation system that combines neural and statistical approaches to machine translation to deliver our users an even higher quality translation that utilizes the complementary strengths of both translation models. The new system first translates users’ queries using both a statistical and a neural machine translation model.  Next, CatBoost, our gradient boosting library ranks the outputs of each model, ultimately selecting the highest quality translation. 

There are several approaches to machine translation and over the years, a number of technological advances have improved the quality of machine translation.  Since its launch in 2011, Yandex.Translate has been powered by statistical machine translation, a widely used approach that works by comparing example translations to find statistical correspondences between words in the two languages.

With today’s launch, Yandex.Translate now also includes a neural machine translation component, a method that has led to more fluent, human-like translations in the last few years. The new Yandex.Translate system is unique in offering users a free machine translation service that combines these two methods.

Statistical translation and neural translation models each have different strengths that complement each other. When combined in our new hybrid machine translation system, they will produce higher quality results than either of the underlying models alone. 

Statistical models prove extremely efficient at memorizing example translations and can produce better translations of words or phrases that are seen less frequently in the training data.  However, statistical machine translation break sentences up into words or phrases during the translation process, which sometimes makes it challenging to construct fluent translations.  

Neural machine translation models, on the other hand, can process entire sentences at once.  Neural models choose a translation based on the full context of a query, often resulting in much more fluent, human-like translations. But, because the neural network uses context to understand how a word is translated, it often fails to learn reasonable translations for words that it saw very few times in the training data. By combining the two systems, which excel in different areas, we see significant improvements in translation quality over either of the individual methods. 

The hybrid system will initially be launched for the English and Russian language pair, which accounts for 80 percent of the tens of millions of daily Yandex.Translate requests. The Yandex.Translate team also hopes to add other language pairs in the near future. 

Yandex’s new Head of Machine Translation, David Talbot explains, “We are excited to launch our new hybrid system for Yandex.Translate users. Ultimately, we want to develop a deeper understanding of how we can better assist Yandex users with their language needs, be it communication, language learning or simply accessing the huge amounts of information on the web available in other languages.”

Currently, Yandex.Translate offers users text, speech, and image translation and supports 94 languages pairs. Start using Yandex.Translate today at https://translate.yandex.ru/ or download the mobile app for iOS and Android.

New Intelligent Search Algorithm “Korolyov” 

 

Today, Yandex released a new version of its search platform, incorporating two important elements:

·      An upgraded version of a deep neural network based search algorithm called “Korolyov,” named after a town northeast of Moscow that has long served as the center of Russia’s space exploration program.

·     Incorporating Yandex.Toloka, a mass-scale crowd-sourced platform for search assessors into  Yandex MatrixNet.

“Korolyov” builds on “Palekh,”  Yandex’s first neural network based search algorithm released in late 2016.  The update improves how Yandex handles infrequent and complex queries, known as long-tail queries, in two distinct ways. 

First, “Korolyov” is better at understanding user intent than its predecessor because it examines the entirety of web pages rather than just their headlines. Second, “Korolyov” can scale to analyze a thousand times more documents in real time than “Palekh.”

Like all modern AI-based systems, “Korolyov” improves itself with each incremental data point. Yandex’s position as the largest search engine in Russia creates a positive feedback loop for our deep neural network algorithm, which leads to superior search results for our users.  

"Korolyov" results feed into MatrixNet, Yandex’s proprietary machine learning ranking algorithm, where a number of other ranking factors are considered before results are returned to a user.  

Recently, MatrixNet started incorporating data from Yandex.Toloka, Yandex's crowdsourcing platform, in addition to anonymized user data to train the machine learning algorithms. 

These updates help us to enhance the quality of our search services for our users.  To learn more about “Korolyov,” you can watch the event broadcast in Russian only

Choosing Yandex search on Android

Starting this month, Android users in Russia are presented with a choice screen in Chrome Mobile browser allowing them to select their preferred search engine.  Previously, Google search was the default search on Chrome on all Android devices; a different search engine could only be selected by accessing the application settings.  Now, with the most recent version of Chrome Mobile (v.60), users are prompted to select their default search engine when the Chrome app launches.  This is a huge milestone for Russian users and something we have been working towards for a long time.

Users are prompted via a new choice screen to select one of three search engines: Yandex, Google or Mail.ru when the updated Chrome Mobile app first launches:

Choice screen here has been translated into English for convenience

As one of the largest internet companies in Europe, and the leading search and mobile applications provider in Russia, access to platforms is critically important to Yandex.  We are excited that Russian consumers can now easily choose their preferred search engine on their Android devices.

Yandex has been the leading search provider in Russia for over 20 years.  We help mobile users when they are on the go by providing them with the highest quality and most relevant information.  We have built our search and other services with a total commitment to serving Russian users’ distinct needs.  As consumers are ever more dependent on their mobile devices for finding information about the world around them, we are excited that Russian users now can now easily choose the search provider best suited to their needs. 

Thanks for choosing Yandex over the years!  We look forward to continuing to help users better navigate the online and offline world! 

New Search Traffic and Browser Usage Analytics Tool: Yandex.Radar

Yandex is proud to announce Yandex.Radar, a new analytics tool that provides the most accurate search engine and browser usage data available for the internet market in Russia, Belarus, Kazakhstan and Turkey. Yandex.Radar allows webmasters to quickly and easily segment search engine and browser usage data by device type and operating system. For example, users can analyze Yandex Browser on different platforms.

Yandex.Radar originated out of Yandex.Metrica, the leading web analytics platform in Russia and the second largest web analytics platform in the world.  Yandex.Radar uses anonymised aggregated data from all sites with a Yandex.Metrica tag. Approximately 60% of websites with Yandex.Metrica installed do not have other analytics tools that provide publicly reported data. Yandex.Metrica’s exclusive coverage creates a rich and broad dataset that allows Yandex.Radar to provide unparalleled accuracy of the provided data.

With Yandex.Metrica installed on 67% of websites in Russia, Yandex.Radar can measure 78% of the Russian internet traffic. This additional data provided by Yandex.Metrica enables significant improvement in the accuracy of our measurements and reporting compared to the other providers. The next leading free web analytics platform can only measure 35% of the traffic of the Russian internet traffic.  

In order to further increase the accuracy of measurements, we carefully monitor traffic data nuances.  Data is adjusted for inaccuracies that appear as a result of various technological changes. For example, during the global transition to HTTPS encryption, we introduced a correction to compensate for some older browsers losing referrers of the traffic from HTTPS to HTTP.  

As an additional measure to ensure the highest possible accuracy and independence, we also exclude all traffic to Yandex sites, such as Yandex.News, Yandex.Market and Yandex.Maps. Yandex.Radar also uses sessions for its metrics rather than visitors because it results in a more accurate depiction of web usage.

This approach provides more accurate data for search and browser shares compared to other existing providers. For example, when comparing search engine market shares in Russia, there is a slight shift in share between search engines because of Yandex.Metrica’s larger and more accurate dataset.

Yandex.Metrica is very excited about the launch of this new tool and looks forward to adding new and additional features and functionality in the future.  Any questions about the data or tool can be sent to askradar@yandex-team.ru

Introducing Yandex CatBoost, a state-of-the-art open-source gradient boosting library

By Misha Bilenko, Head of Machine Intelligence and Research 

Recent developments in machine learning have accelerated its transition from a computer science research area to a technology that drives numerous customer applications. One of the most buzzed about methods leading this transition is deep learning. At Yandex, our homegrown deep neural networks are an important part of the machine learning portfolio that helps sustain our market-leading performance in search, speech recognition and synthesis, vision applications and machine translation. At the same time, we’ve also integrated many other forms of machine learning across our products and services. 

One thing to remember about machine learning is that there is no singular best approach – it is a rich collection of algorithms that each have their own strengths and weaknesses for specific types of data and certain types of customer problems. Deep learning has unlocked amazing capabilities in the advancement of artificial intelligence, but, at the end of the day, it’s just one part of a much broader machine learning tech stack that also includes linear and tree-based models, factorization methods, and numerous other techniques that leverage statistics and optimization. 

Gradient boosting is a machine learning algorithm that is widely applied to the kinds of problems businesses encounter every day like detecting fraud, predicting customer engagement and ranking recommended items like top web pages or most relevant ads. It delivers highly accurate results even in situations where there is relatively little data, unlike deep learning frameworks that need to learn from a massive amount of data. Gradient boosting is ideal for predictive models that analyze many different forms of data, including descriptive data formats with categorical features. In most applications, it is the most powerful “ultimate” model that integrates inputs from many different machine learning techniques, including those from deep learning models. Thus, it is the most important method in a practitioner’s tool case, one that can be used to leverage a wide range of data formats and combine a variety of more specialized models.

Today, we are thrilled to announce that we are open-sourcing CatBoost, a gradient boosting library. It is especially powerful in two ways: it yields state-of-the-art results without extensive data training typically required by other machine learning methods, and it provides powerful out-of-the-box support for the more descriptive data formats that accompany many business problems. Developed by Yandex researchers and engineers, it is the successor of the MatrixNet algorithm that is widely used within our services for ranking tasks, weather forecasting, fraud detection and making recommendations. We believe that it can be applied across a wide range of industrial machine learning tasks, in domains ranging from finance to scientific research. 

CatBoost can be integrated with deep learning tools like Google’s TensorFlow, as demonstrated in the accompanying tutorials, where TensorFlow-trained models for text provide inputs to CatBoost. Models trained by CatBoost can be used in production via Apple’s Core ML framework.  Apps can be built with CatBoost-trained models, bringing intelligent features directly to customers’ devices.  

CatBoost delivers best-in-class accuracy unmatched by other gradient boosting algorithms today. It is an out-of-the-box solution that significantly improves data scientists’ ability to create predictive models using a variety of data sources, such as sensory, historical and transactional data. While most competing gradient boosting algorithms need to convert data descriptors to numerical form, CatBoost’s ability to support categorical data directly saves businesses time while increasing accuracy and efficiency. 

Over the coming months, we will be rolling out CatBoost to benefit the majority of our Yandex services as we strive to deliver the best customer experience we possibly can. For example, today our weather forecasting tool Yandex.Weather uses MatrixNet to deliver minute-to-minute hyper-local forecasts, while in the near future, CatBoost will help provide our users with even more precise weather forecasting so people can better plan for quick weather changes. 

Outside of Yandex, CatBoost is already being used by data scientists at the European Organization for Nuclear Research (CERN) in order to reduce the amount of particle identification errors in data produced by the Large Hadron Collider beauty experiment.

For 20 years now, Yandex has been pioneering innovation in machine learning and artificial intelligence to build intelligent products and services that help consumers and businesses better navigate the online and offline world. We feel it’s our duty to share our expertise in machine learning with the open-source community. By making CatBoost available as an open-source library, we hope to enable data scientists and engineers to obtain top-accuracy models with no effort, and ultimately define a new standard of excellence in machine learning. Learn more at http://catboost.yandex.

Yandex + Uber

This is the letter Tigran Khudaverdyan, CEO of Yandex.Taxi, sent to the team earlier today:

Dear friends,

I’m excited to share some important news. Yandex.Taxi and Uber have agreed to combine their businesses in Russia, Azerbaijan, Armenia, Belarus, Georgia and Kazakhstan. Together, we will continue to build a ride-sharing service that offers a viable alternative to automobile ownership or public transportation.

Here are a few stats for the combined company:

– 127 cities and 6 countries
– 35MM rides in the month of June
– $130MM of gross bookings in the month of June

Many of us who work inside Yandex feel that everyone has already switched to ride-sharing, but in reality, we are just at the beginning of this journey. Our goal is to create a platform that rivals car ownership or public transportation in accessibility and convenience.

Analysts estimate that the size of the official Russian taxi sector is approximately $8.4Bn (2016, VTB Capital). Unofficial or “gypsy cab” sector is estimated at another $1.9Bn (2015, Analytical Center of the Government of Russian Federation). That means that the combined company’s share of the taxi sector would have been only about 5-6%.

Now turning to how this will work from an integration perspective. Following regulatory approval and completion of the deal, for riders, both the Yandex.Taxi and the Uber app will operate as before. Driver apps, on the other hand, will be transitioned to a unified platform, allowing drivers to receive orders both from Yandex.Taxi and from Uber rider apps. This combined driver platform will significantly increase the number of available cars, reduce passenger wait time, and boost vehicle utilization. Drivers will be able to perform more trips per hour while passengers will continue to enjoy affordable prices.

The combined company will benefit from the robust technology stack that we have developed and the world-class navigation and mapping technologies of Yandex. Over the past year we have made several significant leaps in our routing and ride-assignment algorithms that have greatly increased vehicle utilization. For example, now, during rush hour, drivers can perform 30% more trips than before. Unifying our drivers platforms will create an even greater opportunity to improve the quality of our services and further boost vehicle utilization.

In addition, our users will have seamless global roaming across the Uber and Yandex.Taxi platforms. For example, a user of Yandex.Taxi could order an UberX directly from their Yandex.Taxi app upon arriving in London or Bangkok. An Uber user arriving in Moscow from Paris will be able to order a Yandex.Taxi straight from their Uber app. This creates one of the most convenient ride-sharing roaming agreements in the world!

In late May, as many of you have seen we demonstrated an early prototype of our driverless car technology. We will continue developing this project, drawing on the expertise of our engineers in computer vision, image recognition and machine learning. I hope we will have more to share with you in the coming months :)

The new company will also operate the UberEATS service in the region. We will bring together the great international expertise of the UberEATS team and Yandex’s expertise in mapping and pedestrian navigation to improve on the logistical complexities of online food delivery.

As part of the transaction, Uber will invest $225MM and Yandex will invest $100MM into the combined company, valuing it at $3.725Bn on a post-money basis. On a pro forma basis, Yandex will own 59.3% of the combined company, Uber will own 36.6%, and employees will own 4.1%. The two teams will be integrated together and I will serve as the CEO.

As we take this next step and I reflect on everything that we have accomplished, I want to take the opportunity to thank the Yandex.Taxi team that has built an incredible business in a short period of time. The Yandex.Taxi team is one of the strongest teams with whom I have had the privilege to work. I am very excited that we will now be joined by the strong, talented, and successful Uber team. One of our primary goals following completion will be to integrate our two teams and combine our talents.

The transaction is still subject to regulatory approvals and is scheduled to close in Q4’17.

I look forward to working together to build a new company! :)

Tigran Khudaverdyan
CEO
Yandex.Taxi

YandexTaxi1.jpg

Yandex Ramps Up Its Cloud Platform Initiative Yandex.Cloud

Yandex is excited to announce it is ramping up its cloud platform initiative, Yandex.Cloud. Over the last year we have been exploring and experimenting with a cloud initiative that would combine our network of state-of-the-art data centers across Western Europe and Russia, our developer talent, and our deep expertise in machine learning and data mining to provide businesses a high-performance cloud computing platform. Today we are excited to take the next step and further expand on the Yandex.Cloud vision.

As more and more businesses migrate their services to the cloud, cloud-computing technologies are becoming an indispensable part of the enterprise. In the Russian market, the demand for cloud services is growing but there are few high-quality cloud providers serving local businesses. Yandex sees an opportunity to build a cloud solution powered by machine learning that will address this issue locally before we expand our offering globally. 

“Yandex is uniquely positioned to develop a best-in-class cloud computing solution that meets the technological and business needs of enterprises today,” said Yandex CTO Mikhail Parakhin. “We have taken the time to understand the needs of enterprise customers and now we are putting the right infrastructure in place to build a cloud computing solution that delivers against these needs.”

Yan Leshinsky
Yan Leshinsky

Heading the new division is Yan Leshinsky a seasoned engineering leader who brings a wealth of world-class cloud computing expertise and specialization to Yandex. Prior to joining Yandex, Yan managed development teams at Salesforce, Amazon Web Services and Microsoft. Yan’s leadership will accelerate Yandex’s initial cloud experiments towards a highly developed solution on the cloud market.

“There are a number of things that attracted me to Yandex and this role. Yandex has access to an amazing pool of math and engineering talent in Russia, which feeds the high amount of intellectual capital at the company,” said Yan. “Yandex also has an organizational and technological framework centered on the customer. That combination of developer talent and customer focus sets Yandex apart and creates a great atmosphere to build out the Yandex.Cloud initiative.”

Yan’s appointment as the head of Yandex.Cloud is another example of Yandex’s commitment to best-in-class excellence, following Misha Bilenko’s hiring in February to head our Machine Intelligence and Research division. Over the next year, Yan will be working to further develop the Yandex.Cloud vision as well as build out the Yandex.Cloud team. While Yandex initially plans to focus Yandex.Cloud for the local market, we hope to broaden our reach to businesses across globe in the future.

Yandex Data Factory and the Next Industrial Revolution: Steel, Oil, & AI

Jane Zavalishina is the CEO of Yandex Data Factory, a spin-off of Yandex founded in 2014 to provide machine learning solutions to enterprises. In this post, Jane explains how YDF’s business has evolved since its launch, and why industrial AI is now in focus of its strategy.

Since its inception, Yandex Data Factory (YDF) has pioneered an innovative way to create value for companies by applying our expertise in machine learning and artificial intelligence (AI) to help solve their business needs. YDF arose as a solution to the problem many businesses faced at the peak of the big data craze. Essentially, businesses had begun amassing huge amounts of information, but were struggling to extract tangible value from this data.

The solution, of course, is in machine learning. Our parent company, Yandex, was an early leader in machine learning technology and today, machine learning powers 70 percent of Yandex’s products and services. We realised that wherever large stores of data exist, so does the opportunity to use that data to reach measurable business improvements. The same algorithms that power Yandex’s services, can be used to help other businesses improve their operations, revenues and profitability.

Over the past two years, we have worked with a number of companies across multiple industries on various successful projects. Together with our clients, we discovered the best use cases where machine learning can be applied to increase the efficiency of existing processes in a measurable way – be it predicting demand for a retail chain, or using computer vision to cut moderation costs for online service. Along the way, we accumulated a huge amount of expertise on merging data science with business.

One such case included our work with Magnitogorsk Iron & Steel Works (MMK), that marked one of the first ever collaborations of its kind between a technology company and steel company. MMK, one of the world’s largest steel producers, wanted to reduce its production costs while maintaining the same high-quality product. YDF developed a machine learning-based service that recommends the optimal amount of ferroalloys—the ingredients needed to produce specific steel grades. Our predictive system demonstrated the reduction of ferroalloy use by an average of five percent, equating to annual savings of more than $4 million in production costs, while consistently maintaining the same high quality of steel.

Similarly, we are now optimising the operations of a gas fractionation unit for a petrochemical company. Our solution recommends the fractionation unit parameters for maintaining the best performance and energy savings, decreasing costs in the process. Last week, we also signed a collaboration agreement with Gazprom Neft, an integrated oil company. We plan to apply our technologies to well drilling and completion, and other production processes. These successful efforts demonstrate the high potential for collaboration between artificial intelligence and industrial manufacturing.

The industrial sector – responsible for one-third of global GDP – has proven to be the ideal vertical, perfectly positioned for the effective application of our technologies. The industrial sector has become YDF’s focal point through the combination of our own successful application of predictive analytics with industrial data and the fit of the industry. Put simply, manufacturers know the value of optimisation at their hearts. Industrial manufacturing is also a unique cultural fit. They value measurements above opinions, they have perfected integrating new technologies in the existing processes, and they know how to estimate their effect through properly designed experiments.

For decades, the cornerstone of competitiveness in manufacturing has been centered on the optimization of existing processes, reaching for each tenth of a percent of efficiency in each step. And when all traditional optimisation means have been applied, the next efficiency leap of five to ten percent is often prohibitively expensive and equally time-consuming. These improvements typically consist of equipment upgrades with multi-million dollar investments, years spent on construction, rigorous training and implementation, and a lengthy delay before seeing any tangible financial return. Compared to this, receiving the same level of optimization via machine learning in a matter of months with minimal upfront investment is nothing short of revolutionary.

These long-term benefits extend far beyond a simple profit and loss sheet, and can help conserve both human capital and natural resources. By training machines to focus on the mundane, routine decisions that keep a factory running, artificial intelligence and machine learning allow human employees time to tackle more important tasks. By applying these technologies to oil and gas, companies not only achieve time and material savings, they can also reduce their energy consumption by up to 25 percent.

Our AI-enhanced models create endless opportunities to add value to the manufacturing industry. These benefits are especially noticeable in process manufacturing, where materials and mixtures – metals, chemicals, etc. – are produced. Essentially, these are also the industries responsible for the highest resource consumption.

The AI revolution in manufacturing is happening right now, and we are thrilled to be leading the charge. As this future becomes a reality, we’ll be there – at the forefront – blazing new trails in the industrial sector and delivering far-reaching effects for both the companies we work with and the larger communities they serve.