Paul Pokotylo

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

Motion data annotation is a key step in the development of systems that work with 3D dynamics. In particular, motion-capture and procedural animation data form the basis for training models that understand, reproduce, or create motion in space. Motion capture captures real human or object movements using sensors and cameras,

Insurance professionals spend a lot of time manually reviewing policies. Advanced annotation tools now analyze endorsements and clauses. By combining OCR, NLP, and visual understanding language models, these systems find important information from medical records, claim forms, and policy agreements, resulting in faster processing times. Automated data extraction reduces human

Solvency II and IFRS 17 establish clear rules for insurance companies on how to calculate risks, assess liabilities, and present results in their reporting. These standards are designed for transparency and control; however, in practice, they generate vast amounts of data that require processing and verification. The traditional approach of

Insurance companies handle mountains of photos and paperwork every single day. A dented bumper, a water-damaged ceiling, and a half-legible form each case comes with evidence that needs to be checked, classified, and verified. For years, this was the slow part of the job, because someone had to look at

As networks expand, traditional monitoring tools cannot keep up with the speed and complexity of today's networks. This can cost providers millions in downtime or disruptions. When machine learning models are given structured input, they detect disruptions faster than rule-based systems. However, most network operators still rely on