Organisations like yours record loads of data that never gets used. It’s gathering dust in your file systems, even though it can lead you to new insights or allows you to automate parts of your decision-making.
Machine learning uses data to imitate the way that humans learn. However, they can identify trends and patterns way better over larger volumes of data than humans. This allows us to create algorithms that discover insights that your average employee would’ve never found.
Machine learning services are the only way forward to keep your business competitive. It might seem far away, but for lots of businesses, it’s already the driving force for new products and services. We can help you with numerous use cases for machine learning – automating repetitive processes, recommending products for your customers or forecasting the generation of energy or the next need for maintenance are just a few examples.
At Lifely, we have considerable experience in the art of natural language processing, machine learning techniques that deal with written texts. It’s one of the most common ways we store our data, and can be a goldmine for your organisation. Normally, it’s incredibly time consuming for people to work through text within a reasonable time. Algorithms can perform actions like summarization, question answering, language detection and translation within a fraction of the time that a human needs, allowing you and your team to focus on the important things in life, while leaving the mundane tasks to computers.
Within 2 minutes we can help you understand what the impact of different choices in technology, responsibility and longevity are on the budget of your AI model.
Calculate the budget for an AI modelWe’ve all heard the stories of IT projects running over budget. Our machine learning services are structured to follow a rigid problem-solving process in order to give you a tangible AI prototype without running over budget. We first make sure that we’re on the same line: we start every project with a knowledge session to inspire you to learn more about AI, its advantages and what it can mean for you. Then, we plot the context of the model. Only when we know the exact context, we start creating a model that fits your problem best.
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