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Paul Pokotylo

Paul Pokotylo

Paul Pokotylo is an Account Executive, leading strategic initiatives in accelerating AI development. He specializes in custom data solutions for various business applications that drive value.

How annotated data enables Solvency II & IFRS 17 Automation

How annotated data enables Solvency II & IFRS 17 Automation

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 claims AI: annotating documents at scale

Insurance claims AI: annotating documents at scale

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

Building Datasets to Detect Anomalies in 5G Core

Building Datasets to Detect Anomalies in 5G Core

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

Annotating Sensor Data for Utility Equipment Failure Prediction

Annotating Sensor Data for Utility Equipment Failure Prediction

The study of equipment reliability in utility systems increasingly relies on structured annotation of sensor data. Signals collected from transformer health monitoring, vibration analysis, and infrared imagery require systematic labeling to establish a direct connection between measurement patterns and specific failure modes. Without annotation, fluctuations in sensor outputs remain ambiguous,

Edge AI: Cameras and Annotations

Edge AI: Cameras and Annotations

Businesses still rely on manual visual inspections for quality control. This is now replaced by intellectual systems that analyze visual inputs in the source and combine deep training with decentralized processing. Traditional cloud solutions have problems with delay and bandwidth restrictions. Modern approaches unfold a curtain neural network (CNN) on

Edge AI Data Abstract: Optimizing Models for Devices

Edge AI Data Abstract: Optimizing Models for Devices

Most real-time device decisions are now made without the cloud. This is due to the breakthrough in machine learning workflows prioritizing speed and autonomy. Traditional cloud-dependent systems struggle to meet today's demands for instant responsiveness in medical diagnostics and autonomous vehicles. The strategic benefits go beyond speed. By

RLHF Annotation Services: Training AI Models with Human Feedback

RLHF Annotation Services: Training AI Models with Human Feedback

Traditional language models rely on text patterns. Modern systems need to understand intent, context, and ethics. This requires a three-phase process: basic pretraining, supervised refinement, and reinforcement learning optimization. Each phase integrates expert human judgment to align results with real-world needs. Combining computational power with practical application through precise feedback