Scale AI

Manages distributed annotation workflows for computer vision tasks (bounding boxes, segmentation, classification) through a managed workforce with built-in quality assurance layers. Uses consensus-based validation where multiple annotators label the same data and disagreements trigger expert review, combined with automated consistency checks and rework queues to maintain labeling accuracy above configurable thresholds.

Handles sequence labeling, named entity recognition, intent classification, and semantic relationship annotation for text data through a managed annotation interface. Supports hierarchical entity schemas, multi-label classification, and context-aware labeling where annotators see surrounding text and previous labels to maintain consistency across large corpora.

Provides annotation services in 50+ languages with native speaker annotators, supporting language-specific nuances, dialects, and cultural context. Automatically routes tasks to annotators matching required language and dialect, with quality assurance for language-specific tasks like machine translation evaluation and sentiment analysis across languages.

Integrates with client ML models to pre-label data automatically, then routes pre-labeled data to human annotators for review and correction. Reduces annotation time by 40-60% compared to manual annotation from scratch by having annotators verify and correct model predictions rather than labeling from zero. Tracks which examples the model got wrong and uses those for model retraining.

Collects human feedback on LLM outputs (rankings, ratings, binary preferences) to create training data for reinforcement learning from human feedback (RLHF) and model fine-tuning. Manages comparison workflows where annotators rank multiple model outputs, rate quality on custom rubrics, or provide binary preference judgments, with built-in consistency checks and expert review for edge cases.

Manages multi-modal sensor data (camera, LiDAR, radar) annotation and dataset versioning for autonomous vehicle training pipelines. Handles 3D bounding box annotation, sensor fusion labeling, and tracks dataset lineage with version control, allowing teams to reproduce model training runs and audit which data versions were used for which model checkpoints.

Exposes REST and GraphQL APIs for programmatic submission of annotation tasks, status polling, and result retrieval, enabling integration into ML pipelines and CI/CD workflows. Supports batch submission with configurable callbacks, webhook notifications on task completion, and structured result formatting for direct ingestion into training pipelines without manual export/import steps.

Allows teams to define custom annotation schemas (hierarchical taxonomies, conditional fields, multi-type labels) through a visual builder or JSON schema format, with automatic validation to ensure annotators provide complete and consistent labels. Supports schema versioning and migration, allowing schema changes without invalidating previously labeled data.

Automatically calculates agreement metrics (Cohen's kappa, Fleiss' kappa, Krippendorff's alpha) across multiple annotators on the same examples, identifies disagreement patterns, and routes conflicting labels to expert reviewers for adjudication. Provides dashboards showing agreement trends over time and per-annotator reliability scores.

Manages Scale's internal annotation workforce, automatically routing tasks to available annotators based on skill level, language, domain expertise, and current workload. Provides capacity forecasting and SLA management, allowing clients to specify turnaround time requirements (e.g., 48-hour completion) and Scale automatically allocates workforce to meet commitments.

Provides SOC 2 Type II, FedRAMP, HIPAA, and GDPR compliance certifications with encrypted data handling, secure data deletion, and audit logging. Manages data residency requirements (e.g., data must stay in US regions) and provides detailed audit trails showing which annotators accessed which data and when.

Integrates with client ML models to identify which unlabeled examples would be most valuable to label next, using uncertainty sampling and model-based prioritization. Automatically submits high-value examples for annotation and tracks how much each labeled example improves model performance, enabling data-efficient labeling strategies.