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Tatiana Verbitskaya

Tatiana Verbitskaya

Tatiana Verbitskaya is a Solutions Engineer dedicated to solving complex problems. In addition to her client-facing work at Keymakr, she is also an active contributor to the computer vision community.

How Annotated ADS-B and Weather Data Improve Routing Models

How Annotated ADS-B and Weather Data Improve Routing Models

Next-generation routing systems combine two data streams: annotated aircraft location information and hyperlocal weather models. Modern aviation thrives on precision. By enriching ADS-B transponder signals with meteorological context, airlines gain tools for real-time decision-making. These systems analyze everything from wind shear patterns to congested air corridors, allowing route recompute in

Aerospace AI: labeling satellite & aerial imagery for object detection

Aerospace AI: labeling satellite & aerial imagery for object detection

Today, geospatial AI is actively transforming the approach to analyzing satellite and aerial imagery, opening up new opportunities for the aerospace industry. Computer vision (CV) technologies enable the automatic detection, classification, and tracking of objects with high accuracy, a capability that previously required significant resources and manual intervention. Thanks to

Preparing OSS/BSS Data for Machine Learning Models

Preparing OSS/BSS Data for Machine Learning Models

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

5G Network Optimization: AI-Powered Data Annotation

5G Network Optimization: AI-Powered Data Annotation

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

Creating High-Quality Datasets for Utilities & Renewables

Creating High-Quality Datasets for Utilities & Renewables

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 & Wearable AI

Building Curated Datasets for Mobile & Wearable AI

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

Steps to Align Your LLM Program with Regulated Industries

Steps to Align Your LLM Program with Regulated Industries

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