From computer vision to text and document AI, we prepare datasets with clear instructions, reliable review, and a workflow that can grow with your project.
Start a Client ProjectBengal, India | Official connection: sumanasanaroy@gmail.com | LinkedIn
Our services are shaped around real machine learning workflows: define the label set, test it on sample data, fix confusing rules, then scale with review and reporting.
Bounding boxes, polygons, cuboids, landmarks, segmentation masks, classification, and defect labeling for visual AI systems.
Frame-by-frame object tracking, event tagging, movement paths, and temporal labels for surveillance, sports, mobility, and robotics datasets.
Entity recognition, sentiment, intent, moderation, summarization review, response ranking, and search relevance evaluation.
Invoice fields, receipts, IDs, forms, handwritten text review, table extraction, and clean structured outputs for automation teams.
We can follow your existing labeling platform or help prepare a simple workflow. The focus stays on accuracy, consistency, and clear communication, especially when the project includes tricky edge cases.
Guideline writing and improvement after early sample review.
Annotation, validation, and final dataset packaging.
Progress updates for completed volume, blockers, and quality observations.
Scroll through short moving previews with animated label boxes, giving visitors a clearer feel for object review, frame tracking, and quality checking.
Moving frames can be checked with bounding boxes, labels, and reviewer notes before they are used for model training.
Teams can track objects across frames, tag actions, and prepare structured data for computer vision projects.
Reviewers compare edge cases, correct inconsistent labels, and keep datasets useful for real AI systems.