Somewhere beyond code and machine, there is a team
Artificial intelligence on its own is an echo from the future that holds promises of improvement within nearly every industry and for the general public. As machine learning engineers at Modulai, this means we view our company not only as of the joy of our intellectual stimulation but as the mark we want to leave on this world.
Our achievements so far show that by implementing AI in a variety of sectors we can live up to the promise of betterment for people, companies, and employees. This then becomes our purpose, to educate around machine learning and to implement it where we can generate actual value, financially and for the well-being of people. We’re thought leaders, researchers, and entrepreneurs, but most importantly, we’re developers, deploying state-of-the-art AI in production.
We’re pedagogic nerds, meaning we never let our competence get the better of collaboration. On the contrary, our definition of success is when both parties have learned from the project.
By listening closely to our client’s needs we strive to implement solutions that solve their issues and have a long-term positive impact on their customers and their economy. We want to be the partner of choice in producing advanced bespoke AI systems, design- and implementation-wise.
We will meet you where you are
We take full or partial responsibility for the planning, development, and deployment of AI systems. Start-ups and scale-ups hire us to build their first machine learning-based components in their products, while larger companies hire us to build machine learning capabilities and AI departments.
Investments, partnerships, and joint ventures
Alongside traditional consultancy models, we seek partnerships with startups and scaleups where machine learning is a key ingredient for their success. We’re always on the lookout for AI-based solutions that we believe in, can invest in, and help improve and develop. We’re open to creating joint ventures where we take full responsibility for AI development as well as building and scaling the in-house team. Knowledge sharing is a massive part of our mission as a company; therefore, we gladly support startups with advisory services and help them achieve exponential growth. Today, we’re building a portfolio of investments in product-oriented, scalable AI-native companies, that share the Modulai vision.
Since we’re nothing short of extremely curious and interested in what we do, we take the time to develop our own internal products. We implement the latest AI technologies that we see have the potential for long-lasting value. This helps us address common challenges for customers and build reusable solutions.
Natural language processing
Tabular ML and timeseries
Object detection, image classification, semantic segmentation, and related technologies have a variety of use cases ranging from autonomous surveillance to clever functions in smartphone apps and real-time text recognition. Information extracted from images or videos be used for a range of downstream tasks, whether it is to keep track of the position and velocity of an object, identify anomalies or find patterns.
Data in the form of text is abundant. Companies have access to thousands, if not millions of internal documents. The value in these and other accessible bodies of text can be leveraged to create value through a multitude of downstream tasks, such as automatic question answering, sentiment analysis, intelligent search, and automated communication.
Machine learning on structured data from relational databases and spreadsheets to data produced by sensors is a mature subfield of AI. Downstream tasks include everything from logistics optimization to economic forecasting. Models range from lightweight and linear to complex algorithms taking into account the values in hundreds of variables.
Personalization and AI for retail
AI for Medtech
AI for Fintech
Aside from creating personalized recommendations, there is a range of opportunities for making retail companies more profitable. The increasing bottom line comes from increasing revenue as well as streamlining operations, both can be addressed with ML. We have worked with a long-range of B2B and B2C retail companies and helped them transform their business with the use of AI. Further, personalization increase the user experience in a variety of other sectors, such as content and streaming platforms, ad networks, and social networking platforms.
The Medtech space is close to our hearts, and we’re proud to have been part of many successful AI applications in the space. Using object detection, image classification, time series classification, AI explainability, and more we have built applications for diabetes, rheumatoid arthritis, cardiology, skincare as well as dental health. Medtech applications range from decision support for medical professionals to instant feedback for patients.
In this space, we have developed models for personal credit risk assessment, fraud detection, company default risk as well as macroeconomic forecasting. A wide range of different machine learning techniques can be used on different data sources to predict economic performance, assess various sources of risks, and for targeted marketing. We know that to be successful in applying AI in the fintech space you have to know the business as well as ML – knowledge we know very few others can match.