Tatiana Verbitskaya

Telecom networks generate vast amounts of data daily, yet few operators fully leverage this information for automation. This results in fragmented systems, holding critical analytics in silos, and leaving networks reactive instead of predictive. Modern networks require intelligence that spans operations and business. Traditional approaches fail to meet customer expectations

The rapid expansion of 5G networks brings new opportunities for ultra-fast connectivity, but achieving consistent performance requires precise optimization. AI-powered data annotation enables operators to process massive volumes of network telemetry, turning raw data into actionable insights. By applying accurate labeling to critical parameters such as network slicing, beamforming, quality

The energy sector is increasingly driven by the ability to collect, structure, and analyze vast operational data streams. Utilities and renewable energy providers rely on AI to convert SCADA logs, smart meter data, and other sensor readings into actionable insights. Properly structured datasets allow operators to anticipate issues, optimize generation,

Building curated datasets for mobile and wearable AI requires carefully balancing data quality, device limitations, and real-world variability. A well-structured dataset must capture diverse usage scenarios while ensuring that models can be efficiently deployed on low-power devices. Techniques such as lightweight CNN architectures are often selected to meet strict constraints

Adapting to the regulatory landscape requires strategic partnerships. Therefore, the methodology should focus on building secure, audit-ready frameworks tailored to your operational requirements. Regulated industries face unique challenges in implementing AI. Each implementation requires careful planning, from data privacy mandates to industry reporting standards. Combining technical rigor with regulatory awareness

In 2025, data annotation continues to serve as a foundational element in developing large language models (LLMs), directly impacting their accuracy, reliability, and alignment with human intent. As LLMs are increasingly applied across specialized industries and high-stakes environments, the demand for high-quality, context-aware annotation has grown significantly. Key Takeaways * High-quality