Multimodal Data Annotation & RLHF Project (Computer Vision + NLP)
Delivered end-to-end AI data annotation and evaluation services for multimodal datasets, including image, video, and text. The project involved large-scale annotation tasks such as bounding boxes, polygon segmentation, classification, and quality validation for computer vision models. Additionally, we supported NLP and RLHF workflows, including prompt evaluation, response ranking, and dataset refinement for model alignment. The dataset size ranged from thousands to tens of thousands of samples, with structured guidelines followed to ensure consistency and accuracy. Our team maintained strict quality control processes, including multi-level review, auditing, and feedback loops to consistently achieve high accuracy benchmarks (90%+). We worked with tools such as CVAT and Labelbox, ensuring efficient workflow management and annotation consistency. Daily production targets and turnaround timelines were maintained, with scalable team allocation based on project requirements. This project demonstrates our ability to handle both pilot and large-scale annotation programs with reliability, speed, and quality assurance.