Computer Vision Services

It involves expert reviewers validating and correcting annotations, improving data quality and model performance.

Drawing rectangles around objects; ideal for detecting and localizing objects like cars, people, and products.

Cuboid captures depth and volume for 3D representation, while bounding boxes only provide flat 2D outlines.

It uses multiple points to tightly outline irregular shapes, ensuring high precision in object segmentation.

It marks specific joints or features (like eyes, elbows), enabling pose estimation or facial expression tracking.

For marking linear features like roads, lane markings, or pipelines—common in autonomous navigation.

Classifies every pixel into object categories; useful for scene understanding and autonomous driving.

Each object instance is uniquely labeled; used by Mask R-CNN and other advanced detection models.

Multi-layer reviews, inter-annotator agreement checks, and tool-based QA metrics ensure accuracy.

Tools and workflows are tailored using client guidelines, platform integrations, and rule-based logic.

Automotive, healthcare, agriculture, retail, surveillance, manufacturing, and smart cities.

Secure VPN access, role-based controls, encrypted data storage, and strict NDA protocols.

Yes, including point-level tagging, cuboids in 3D space, and sensor fusion support.

Varies by scope, but typically 1–2 weeks for 10,000+ images with scalability based on project urgency.

Work is modularized and distributed across trained teams in parallel with centralized QA.

Both. We support client tools (e.g., CVAT, Labelbox) and offer in-house proprietary platforms.

Share dataset & scope → pilot run → quality review → full-scale execution → regular reporting.