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Banks, investment firms, insurance companies, and fintech organizations handle vast amounts of structured and unstructured information every day: financial statements, analytical reviews, market news, regulatory documents, and client requests. Large language models of the next generation open up qualitatively new opportunities for automation, analytics, and decision-making. Specialized LLMs for finance

The integration of LLMs into the healthcare sector has become a natural response to the growing volume of medical information. Modern medicine generates a colossal amount of unstructured data, most of which is stored in text format. The ability of AI to effectively process, analyze, and systematize these arrays allows

Creating a universal autonomous driving system is impossible without considering the global context, as artificial intelligence algorithms directly depend on the diversity of input data. A model that demonstrates flawless performance on the perfectly marked highways of California can become dangerous in the snowy conditions of Scandinavia or the chaotic

Alignment of large language models (LLM) is performed through a complex RLHF pipeline. This approach is efficient, but is associated with high costs and technical difficulties. Direct Preference Optimization (DPO) offers a simple, transparent alternative for preference optimization and effective model alignment techniques. This approach preserves the advantages of RLHF,

Reinforcement Learning with Human Feedback (RLHF) is a key approach to aligning large language models (LLMs) with human values and expectations. Rather than relying solely on automated metrics, RLHF uses human ratings to train models to select relevant responses. This method enables LLMs to generate grammatically correct text and exhibit

Creating reliable autonomous driving systems requires colossal amounts of labeled data. However, collecting real-world information from roads has significant limitations. Real trips are expensive due to costs for fuel, equipment, and driver labor, and the annotation process takes months. Furthermore, it is nearly impossible to safely capture critical situations on

The stage of evaluating LLM responses is the most critical stage of development, as it is impossible to improve a product without an objective measurement of quality. However, with the evolution of technology, traditional verification methods have faced serious challenges that make them ineffective in today’s market conditions. Manual

Micromobility annotation is the labeling of cyclists, pedestrians, scooters, skateboards, bicycles, and other micro-mobility devices for training urban mobility AI in complex urban scenarios, including accurate e-scooter detection. The main goal is to transform complex street scenes into structured data through bounding boxes, keypoints, segmentation, and tracking, enabling reliable e-scooter

Pedestrians are the most unpredictable and vulnerable category of objects in computer vision systems. Unlike cars, whose trajectory is limited by the roadway and physical characteristics, human movement can change instantly in any direction. That is why high-quality pedestrian labeling has become the foundation for safety in the fields of

In modern search systems and large language models, semantic similarity embeddings play a key role, enabling computers to work not only with words but also with content. Thanks to text and word embeddings, text, documents, and queries are transformed into semantic vectors, in which the proximity between vectors reflects semantic

Modern large language models often operate only on the information they absorbed during their training. This creates serious limitations, as any model's knowledge is "frozen" at the moment its training concludes. If an event happened yesterday, it simply does not exist for the neural network, making

If you ask artificial intelligence about your company’s internal policy, which it has no idea about, it answers with fabrications. Large language models are trained on general information from the internet and do not have access to your internal, private data. This leads to errors, often referred to as

Agents based on Big Language Models (LLMs) are now changing the way businesses, researchers, and developers interact with data and make decisions. The success of these agents depends on the model architecture, quality, diversity, and management of the training data. This guide provides an overview of the sources, generation methods,