Precise, consistent, and scalable data annotation for machine learning models - from raw data to production-ready training datasets, handled by domain-expert annotators with rigorous quality control.
Developed by engineers from leading robotics & automation companies
Bounding boxes, polygons, and semantic segmentation at scale
Curated datasets that directly improve model accuracy and generalization
Multi-pass review, automated validation, and inter-annotator agreement scoring
Machine learning models are only as good as the data they are trained on. Oibil provides end-to-end data annotation and curation services for teams building AI systems across computer vision, natural language processing, robotics perception, and industrial quality inspection.
Our annotators are domain-trained, not generalist crowd workers. Every dataset we produce goes through a structured quality assurance pipeline before delivery. We handle the full data lifecycle - collection guidance, annotation, validation, and ongoing curation as your models evolve.
Bounding boxes, polygons, semantic segmentation, instance segmentation, keypoint annotation, and tracking across video frames. Supports COCO, Pascal VOC, YOLO, and custom formats.
Named entity recognition, intent classification, sentiment labeling, relation extraction, coreference resolution, and instruction-response pair creation for LLM fine-tuning.
3D bounding box annotation, semantic segmentation of point clouds, and object tracking for autonomous vehicle, robotics, and industrial inspection datasets.
De-duplication, outlier removal, class rebalancing, and annotation consistency audits. We identify and resolve labeling errors that silently degrade model performance.
Defect annotation for quality inspection models, robot workspace labeling, object detection datasets for pick-and-place systems, and weld seam segmentation.
Version-controlled dataset delivery, annotation guideline documentation, and ongoing dataset expansion and maintenance as your model requirements evolve.
Quality is enforced at every stage, not reviewed at the end. Our annotation pipeline includes inter-annotator agreement checks, consensus labeling for ambiguous cases, automated validation scripts, and final human review by a senior annotator.
Every annotation is reviewed by at least one independent annotator. Disagreements are escalated to a domain expert for resolution.
Scripts check for label consistency, bounding box integrity, class distribution, and format compliance before delivery.
Detailed, versioned annotation guides produced for every project. Edge cases are documented and resolved systematically, not left to individual annotator judgment.
We review model error analysis with your team to identify annotation improvements that directly address model failure modes.
Defect classification and localization datasets for vision systems in manufacturing - surface defects, weld inspection, PCB anomalies, and dimensional verification.
Object detection and pose estimation datasets for robotic manipulation, bin picking, and navigation in unstructured industrial environments.
Multi-sensor annotation for cameras, LiDAR, and radar. Obstacle detection, lane marking, drivable area, and semantic scene understanding.
OCR correction, form field extraction, document classification, and structured data extraction for enterprise automation pipelines.
Contact us with your dataset requirements - modality, volume, annotation type, and timeline. We will respond with a project proposal within one business day.
Contact sales@oibil.com