About the service
I'm a data annotation and labeling specialist, and for the last few years my daily work has been turning raw and chaotic data into training sets you can trust. I draw precise bounding boxes and polygons on images for object detection and segmentation, classify and label texts, extract named entities for NER, and annotate audio for speech and sound recognition. My work begins long before the first label: together with you we agree on clear labeling instructions, rules for edge cases, and taxonomy, because most model failures I've encountered don't come from the model itself, but from inconsistent and ambiguous labels it's fed. Consistency and quality control are the heart of what I offer. I work in small verifiable batches, cross-check against a reference set, and measure annotator agreement so you always understand how reliable the data is. Every disputed case is documented, discussed, and returned to the instructions, so the dataset becomes more precise as it grows rather than blurring. I work freely in common annotation tools and adapt to whatever platform is already in your pipeline, delivering results in the format your training code expects — COCO, YOLO, JSONL, or your own schema. Whether you need a quick pilot to validate your labeling scheme or a continuous partner who keeps a growing dataset clean and versioned, I treat your data as the foundation of your model and label accordingly. I communicate clearly, meet deadlines, and flag data problems in advance rather than guessing silently. — Bounding boxes, polygons, and semantic segmentation of images — Text classification, sentiment and intent labeling — Named entity recognition (NER) and span marking — Audio annotation, transcription, and sound event labeling — Labeling instructions, taxonomy design, and edge case rules — Quality control: reference sets, annotator agreement, review passes — Export in COCO, YOLO, JSONL and custom schemas
