SERVICES

Transform Your AI with Precision Data

Image Annotation

We have a team of highly skilled in-house annotators with expertise across diverse industries. Equipped with advanced tools and technology, they deliver precise and high-quality labeling for complex projects. Our workforce includes medical professionals, agronomists, engineers, and other domain specialists, ensuring that every project is handled with the right knowledge and accuracy.

Video Annotaion

We handle video datasets of any scale, using bounding boxes, key points, lines, polygons, and multi-segment lines to accurately annotate each frame. Our teams are flexible and can be rapidly scaled to meet the demands of any project, ensuring both efficiency and precision.

Data Collection

We leverage diverse techniques to gather high-quality, unique, and fully compliant data tailored for your training needs. Through our strong network of trusted partners, we are able to address complex requirements across highly regulated industries, including healthcare, waste management, manufacturing, and more.

Data Validation

We enhance the quality of your AI training datasets by validating information and applying human-in-the-loop methods to boost efficiency. With our dedicated in-house team and rigorous security protocols, your data is always protected and never shared with third parties.

Semantic Segmentation

Using our proprietary annotation platform, we deliver pixel-perfect labeling for both images and videos. With advanced segmentation techniques, we enable computer vision systems to capture fine details, recognize subtle patterns, and achieve higher levels of accuracy and performance.

Image Segmentation

Our process ensures that every pixel is precisely annotated, enabling your AI to interpret entire scenes instead of isolated fragments. With advanced proprietary tools, we deliver fast and highly accurate segmentation for image datasets of any scale.

Generative AI

We generate pre-labeled synthetic data using advanced generative AI models, enabling the creation of large volumes of images and videos to accelerate training. Since these models are still evolving, every output undergoes strict human oversight and quality checks to ensure reliability and accuracy.

NLP & Document Annotation

We deliver specialized annotation services for natural language processing, with a strong focus on financial compliance and document analysis. Our expertise helps improve machine understanding, ensuring greater accuracy and reliability in NLP applications.

3D Point Cloud

We specialize in LiDAR data to train AI models on spatial relationships in the real world. This is particularly crucial for industries like automotive, logistics, and aerospace, where autonomous navigation relies on high-quality Point Cloud datasets.

Image & Video Annotation

Bounding Boxes

Bounding boxes are the most common annotation type for object recognition and tracking. Widely used in computer vision, they provide simple yet effective tags for identifying and localizing objects within images or videos.

Rotated Bounding Boxes

Rotated bounding boxes are angled annotations that help your model precisely understand an object’s exact position, improving detection accuracy for objects that aren’t perfectly aligned.

Cuboid

3D cuboid annotation helps extrapolate three-dimensional objects from 2D images or videos by adding depth and height, providing approximate real-world dimensions for more accurate model understanding.

Polygon

Helps precisely define non-standard objects by tracing their shape with small connected lines that define them. Used for added accuracy.

Semantic Segmentation

Semantic segmentation assigns a unique color to every object in an image, enabling your model to understand entire scenes with pixel-level accuracy.

Skeletal

Semantic segmentation assigns a unique color to every object in an image, enabling your model to understand entire scenes with pixel-level accuracy.

Key Points

Semantic segmentation assigns a unique color to every object in an image, enabling your model to understand entire scenes with pixel-level accuracy.

Lane

Linear structures like roads, pipelines, railway tracks, and other infrastructure elements are annotated with lanes to simplify processing and improve model accuracy

Instance Segmentation

Scenes are further classified by assigning unique labels and colors to each object instance. This adds detailed properties and enhances the precision of the annotated data.

Bitmask

This technique is used to label separate parts of an object while treating them as a single entity for example, a field divided into sections by an object in the foreground.

3D Point Cloud

Commonly applied to LiDAR data in autonomous vehicles, this technique reconstructs full 3D scenes while preserving real-world spatial relationships between objects.