Data Annotation
Our mission is to empower the next generation of AI by delivering high-quality, multilingual, and human-verified data solutions. We help companies build smarter, fairer, and more inclusive technology.
3D Cuboid Annotation
At Jeenish AI Solutions, we offer 3D cuboid annotation to help AI systems understand object dimensions and orientation in three-dimensional space. This technique involves drawing precise 3D boxes around objects like vehicles, furniture, or pedestrians in LiDAR, depth, or multi-view imagery. It’s essential for applications like autonomous driving, warehouse robotics, and AR/VR scene understanding.
Our annotators ensure that each cuboid aligns correctly with the object's edges and depth, providing accurate spatial context. The data we deliver integrates seamlessly with 3D perception models. This level of precision improves navigation, object avoidance, and real-world AI reasoning.
Request a DemoPolygon & Polyline Annotation
At Jeenish AI Solutions, we provide precise polygon and polyline annotation to capture the exact shape and boundaries of complex or irregular objects in images. Unlike simple bounding boxes, polygons trace detailed contours—ideal for labeling items like road lanes, rooftops, clothing outlines, and plant canopies.
Polyline annotation is commonly used to mark continuous structures like power lines, lane dividers, and boundaries in aerial or street-level imagery. These annotations are essential for applications in autonomous driving, geospatial mapping, and agricultural AI.
We ensure high accuracy through manual precision and multiple validation layers, delivering pixel-level data suited for segmentation and edge-detection models.
Keypoint & Landmark Annotation
At Jeenish AI Solutions, we offer keypoint and landmark annotation to precisely mark specific points of interest on objects—like facial features, human joints, or vehicle parts. This technique is essential for AI models that perform facial recognition, pose estimation, gesture tracking, or object deformation analysis. For instance, in healthcare, keypoints help track skeletal movement during physiotherapy. In AR applications, facial landmarks enable filter alignment or emotion detection. Our team ensures accurate placement and consistent labeling across frames, supporting both 2D and 3D datasets. These annotations power high-performance models in computer vision systems across sectors like sports analytics, automotive safety, and virtual reality.
Request a DemoImage Classification
At Jeenish AI Solutions, we deliver high-quality image classification services that assign category labels to entire images, enabling machines to understand visual content at a glance. This is crucial for tasks like recognizing product types in e-commerce, filtering inappropriate content on social media, or diagnosing medical imagery.
Each image is reviewed and tagged with consistent, accurate labels based on your taxonomy—whether it’s binary (e.g., “cat” vs. “dog”) or multi-class (e.g., “shirt,” “pants,” “jacket”). Our expert annotators ensure labeling accuracy even across subtle visual differences.
These datasets help train classification models that power search engines, recommendation systems, and automated inspection tools in various industries.
Semantic Segmentation
At Jeenish AI Solutions, we provide semantic segmentation services that label each pixel in an image with a class, allowing AI models to understand the full scene at a granular level. Unlike bounding boxes or polygons, this method gives precise object boundaries—ideal for complex environments. It’s commonly used in autonomous driving (e.g., separating roads, sidewalks, and vehicles), medical imaging (e.g., segmenting organs or tumors), and industrial automation (e.g., identifying parts on assembly lines). Each annotation is manually verified to ensure pixel-level accuracy. We support custom class taxonomies and deliver in standard formats like PNG masks or JSON, ensuring seamless integration into your training pipeline.
Request a DemoVideo Frame Annotation
At Jeenish AI Solutions, we provide video frame annotation services to label objects, actions, or features across consecutive frames in a video. This helps AI models recognize movement, detect patterns, and understand temporal changes in visual data. Our annotators label keyframes and interpolate annotations across sequences, saving time while maintaining accuracy. Common use cases include driver behavior analysis, sports player tracking, and security footage interpretation. We support annotations like bounding boxes, segmentation, or landmarks—delivered in formats compatible with leading video analytics frameworks.
Object Tracking in Video
At Jeenish AI Solutions, we provide object tracking in video, which involves identifying and following objects—such as vehicles, people, or animals—across multiple frames. This allows AI models to understand movement, behavior patterns, and interactions over time. Object tracking is vital for applications like autonomous navigation, traffic monitoring, sports analytics, and surveillance systems. Our annotators ensure temporal consistency, meaning each object retains a unique ID from frame to frame. We support tracking with bounding boxes, segmentation, or keypoints, and deliver datasets optimized for training deep learning models on temporal behavior analysis.
Request a DemoActivity Recognition
At Jeenish AI Solutions, we offer activity recognition services that involve labeling human or object actions in videos—such as walking, waving, driving, or interacting with products. These annotations enable AI systems to interpret behavior and context in real time. This is crucial for use cases like smart surveillance, fitness tracking, driver monitoring, and retail behavior analysis. Our annotators review frame sequences to detect and tag actions with high temporal accuracy. We support multi-class activity tagging and custom taxonomies, delivering data that improves the performance of action detection and video classification models.