Production-Grade
Data Annotation
Your model quality depends on label quality. We deliver human-labeled datasets for NLP, computer vision, document, audio, and video tasks using written guidelines, pilot batches, and quality checks.
This service is for ML teams, research labs, and startups that need dependable data annotation without managing a crowd workforce. Each batch has a fixed scope, output format, review process, and delivery timeline.
Capabilities
Annotation for common ML data types.
We handle text, image, document, audio, and video annotation with workflows matched to each data type. Complex multi-modal work is scoped separately before production starts.
Text Annotation
Named entity recognition, sentiment analysis, intent classification, text categorization, relation extraction, and coreference resolution. Supports 12+ languages when suitable reviewers are available for the scope.
Image Annotation
Bounding boxes, polygon segmentation, keypoint detection, semantic labeling, and instance segmentation. We define annotation rules and acceptance checks before production labeling begins.
Document Annotation
Table extraction, form field mapping, OCR correction, and structured labeling for invoices, receipts, forms, and contracts.
Audio Annotation
Speech transcription, speaker diarization, emotion labels, audio event classification, and review-ready segment metadata.
Video Annotation
Object tracking, action recognition, temporal segmentation, frame-by-frame labeling, and activity detection with clear frame rules.
98%+
Average annotation accuracy across all projects
3x QC
Triple quality check on every annotation batch
48hr
Turnaround for pilot batches and sample reviews
8+
Output formats supported natively
Output
Your format. Our delivery.
We deliver in the schema your training pipeline expects. Standard formats ship by default; custom schemas are configured during the pilot phase.
JSON / JSONL
Universal
COCO
Computer Vision
CSV / TSV
Tabular
spaCy / CoNLL
NLP
YOLO
Object Detection
Pascal VOC
XML Format
Parquet
Big Data
Custom
Your Schema
Pipeline
How annotation works.
Every project follows a structured pipeline with built-in quality gates. You see sample outputs, issue categories, and quality notes before full production.
Requirements & Sample
Send data samples, annotation guidelines, and target format. We review feasibility and return a fixed-scope pilot plan within 24 hours.
Pilot Batch
We annotate a small batch (50-100 samples) for your review. This calibrates edge cases, label definitions, and quality expectations.
Production Run
After pilot approval, we scale production with trained annotators and quality checks. Every batch is reviewed before delivery.
Delivery & Iteration
Annotated data is delivered in your format with quality notes. Additional batches or guideline changes are scoped separately.
Need labeled data
with clear QA?
Send us a sample dataset and your annotation guidelines. We respond with a pilot plan, timeline, and fixed quote within 24 hours.
spacedrift.contact@gmail.com