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Data annotation is the process of labeling, structuring, or organizing raw data to make it understandable to artificial intelligence and machine learning algorithms. Simply put, people or tools add information to data, and then an AI model learns to interpret it correctly. Properly labeled data is a key element in

Building an effective learning support system begins with creating datasets for models predicting student dropouts and retention. The datasets for the models combine academic performance, behavioral activity in educational systems, and contextual factors, allowing the model to detect early signals of risk. A high-quality combination of different data types enables

In the modern world, where pupils and students write thousands of essays and short answers, a significant problem arises: how to check them quickly and objectively? Manual checking has always been slow and expensive, and, most importantly, it involves a subjective element. The scores for the exact text can vary

In a world where education is becoming increasingly individualized, we cannot afford to treat learning materials as an indivisible mass. When all books, videos, and tests are lumped into one "pile", without specifying the topic, level, or the skills they develop, this is unstructured content for an intelligent

Automation of waste sorting processes is becoming increasingly important in the context of the development of computer vision technologies and the need to increase recycling efficiency. The creation of high-quality image sets is a key element for systems that perform material classification, contamination detection and object analysis in conveyor vision

In the modern world, investors and society demand transparency from companies not only regarding profits but also regarding their impact on the environment, society, and governance. These aspects are united under the concept of ESG. The problem is that companies present their ESG reports as long, unstructured texts, such as

Deforestation Monitoring for AI-powered Tagging utilizes a decadal set of satellite-derived climate data and related information to detect habitat loss over multiple years. It combines programmatic access, standard frameworks, and native Python tools to enable teams to quickly discover and process information. This approach reduces noise and seasonality by utilizing

Effective carbon footprint calculations begin with robust data annotation, which forms the backbone of AI models. In an era where transparency and accountability for greenhouse gas emissions are essential for businesses and government organizations, well-organized and structured data labeling not only facilitates accurate climate impact assessment but also streamlines internal

Today, any large company or state institution, whether a bank, a pharmaceutical company, or an environmental agency, constantly faces an avalanche of new laws and regulations. The problem is simple but massive, as information about all these updates is scattered everywhere. It is contained in a vast number of sources,

Legal investigations increasingly rely on automated tools that can process thousands of emails and documents in a matter of hours. Machine learning enables systems to quickly identify patterns, highlight relevant information, and reduce the workload on analysts. Manual verification remains a crucial step, ensuring the accuracy and contextual relevance of

AI systems that analyze historical court records now predict case outcomes. Machine learning is now deciphering patterns. What started as basic statistical models now predict outcomes across jurisdictions. Quality matters most. Careful curation separates useful information from digital clutter. That's why it's important to check every

The legal industry has traditionally been one of the most conservative regarding technological innovations, but today it faces a data overload crisis, too. Lawyers manually review hundreds of pages of complex contracts, especially during auditing. This not only consumes a lot of precious time but also increases risks. The human

The rapid development of artificial intelligence has found one of its most significant applications in aviation. Precision is an absolute priority in an industry where every day of aircraft downtime costs millions, and the price of error is measured in lives.  At the crossroads of advanced technologies and stringent safety