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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,

The rapid growth of online gaming has transformed it into a global social space where players frequently communicate, collaborate, and create shared narratives. However, like any online space, it faces challenges: offensive messages, hate speech, harassment, and misinformation. A safe space is the foundation upon which virtual communities must primarily

Modern games are undergoing a real revolution thanks to AI - especially in how non-player characters (NPCs) are created, react, and evolve, as well as how their behavior and navigation in space are built. NPC behavior is based on prescribed scripts: the character patrols a specific route, responds to triggers,

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

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

The main goal of such datasets is to train the model to recognize obstacles, estimate distance, and safely plan a flight path. The most common types of data are images from cameras, which can be regular RGB, infrared, or stereo, allowing you to see the depth of the scene. Depth

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

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

Keymakr announces a strategic partnership with Segments.ai, a leading labeling platform designed for 3D point clouds and multi-sensor formats. This collaboration aims to streamline labeling workflows, enhance accuracy, and reduce the time required to prepare large datasets, a critical factor for autonomous driving and other industries relying on 3D

Combining traditional financial analysis with innovations such as autonomous vehicle safety modeling is changing data processing strategies. The shift from manual processes to automated solutions requires structured, validated information that delivers accurate predictions. Integrating machine learning accelerates analysis, but success depends on data preparation. Prioritize standardization and cleansing to reduce

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

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

Mobile networks today handle millions of signals every second. These signals include everything from user activity to system performance, and together, they create an overwhelming amount of raw data. Going through such information line by line would be impossible for a human engineer. Automated annotation solves this problem by sorting