Largest data labeling operation; powers OpenAI/Meta/DoD; RLHF specialist; managed workforce of 240K+; Donovan gov platform; Scale GenAI Platform for LLM evaluation
Labeling
4 full, 0 partial of 4
Multimodal Annotation
Support for labeling images, video, text, audio, documents, 3D/DICOM, and geospatial data. Breadth of annotation modalities in one platform.
Full
AI-Assisted Labeling
Model-assisted pre-labeling, active learning, auto-label suggestions. How much AI accelerates human labelers vs pure manual annotation.
Full
RLHF & Preference Data
Preference ranking, rubric scoring, SFT datasets, and human feedback workflows purpose-built for LLM alignment and fine-tuning.
Full
QA & Review Workflows
Consensus labeling, review queues, inter-annotator agreement, AutoQA, and structured rework flows for maintaining label quality at scale.
Full
Automation
1 full, 2 partial of 3
Programmatic / Weak Supervision
Labeling functions, heuristics, and weak supervision to generate training labels at scale without manual annotation. Snorkel-style approach.
Partial
Synthetic Data Generation
Generate synthetic training data (images, text, tabular) to augment real datasets. Addresses data scarcity and privacy constraints.
Partial
Active Learning
Smart sample selection — surface the most impactful unlabeled data to annotators. Reduces labeling cost while maximizing model improvement.
Full
Platform
4 full, 0 partial of 5
Dataset Management
Versioning, slicing, snapshots, lineage tracking, and catalog for reproducible experiments. Export to standard ML formats.
Full
Workforce Management
Managed labeling services, BPO integration, annotator performance tracking, and throughput analytics. 'I need people to label' vs 'I have my own team.'
Full
Security & Compliance
SOC2, HIPAA, GDPR, on-prem/air-gapped deployment, data encryption, and audit trails. Critical for healthcare, finance, and government.
Full
MLOps Integration
Integration with training pipelines (SageMaker, Vertex AI, HuggingFace), model registries, and experiment trackers. Feedback loop from model to labeling.
Full
Cost & Pricing Model
Pricing transparency and predictability. Full = transparent self-serve tiers. Partial = custom enterprise pricing. None = opaque project-based.