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LLM is a large language model (large language model meaning), a type of language model trained on massive amounts of text to predict and generate natural language. These models are powered by core assistants like ChatGPT, Gemini, Claude, and Perplexity. They provide summarization, translation, reasoning, and conversational interfaces that transform

In recent years, large language models (LLMs) have become the backbone of modern AI. They power chatbots, assistants, search engines, business automation tools, scientific research, and even creative platforms. What was once considered an experiment a few years ago has now evolved into a robust and mature infrastructure for working

The modern robotics landscape is based on specialized software that enables machines to see the world, make decisions, and safely interact with complex environments. As robots move from controlled laboratory settings to factories, warehouses, farms, medical facilities, and public spaces, the software infrastructure behind their operation becomes increasingly complex and

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