Product
Lean Data Engine
Collect, curate, and annotate data. Train models and evaluate. Repeat.
The full ML data lifecycle, from raw collection to production-ready training sets, managed by a system built for quality at scale. Up to 80% of AI project time is spent on data preparation. We make that 80% a competitive advantage.
500M+
Data points annotated
99.2%
Annotation quality rate
100K+
Skilled annotators
6
Data modalities supported
Core Capabilities
Everything your data pipeline needs.
Multi-Modal Annotation
Label text, images, video, audio, 3D LiDAR, and documents through a unified interface. Configurable quality tiers for every modality.
Human-in-the-Loop
Combine automated pre-labeling with expert human review. Every annotation carries confidence scores, reviewer IDs, and full audit trails.
RLHF and Preference Data
Collect pairwise comparisons, ranked responses, and critique annotations. Purpose-built pipelines for fine-tuning and aligning large language models.
Generative AI Data Services
Create complex prompt-response pairs, red-team model outputs, and build evaluation datasets from scratch. Generation, RLHF, and red teaming, end to end.
Synthetic Data Generation
Programmatically generate diverse, edge-case-rich training sets for domains where real-world data is scarce, sensitive, or imbalanced.
Data Curation and Versioning
Explore datasets through natural language search. Prioritize data slices, curate for target scenarios, and track full version history with rollback.
Model Evaluation Datasets
Build domain-specific benchmarks to test models against your actual use cases. Measure performance over time and identify weaknesses at granular levels.
Quality Assurance
Inter-annotator agreement metrics, gold standard validation sets, automated rejection workflows, and calibration sessions built into every project.
ML-Assisted Labeling
AI-assisted annotation tools accelerate throughput while maintaining quality. Subject matter experts handle edge cases and high-complexity tasks.
Use Cases
Built for every stage of AI development.
LLM Pre-training and Fine-Tuning
Curate and clean web-scale corpora for foundation model training. Produce instruction-tuning datasets with diverse prompt-response pairs and precise task specifications.
Reinforcement Learning from Human Feedback
Structured human preference collection, response ranking, and reward model training pipelines. The same RLHF methodology used by frontier AI labs.
Computer Vision and Perception
Bounding box, polygon segmentation, semantic segmentation, keypoint, and panoptic annotation for vision model training across automotive, robotics, and surveillance domains.
Document AI and Information Extraction
Annotate structured and unstructured documents, extract named entities, classify intent, and build ground-truth datasets for document understanding models.
Model Red-Teaming and Safety
Adversarial prompt generation, model vulnerability identification, bias auditing, and safety evaluation datasets for responsible AI deployment.
Speech and Audio
Transcription, speaker diarization, sentiment labeling, and sound event detection annotation at scale for voice and audio model development.
How It Works
A repeatable loop that improves with every iteration.
01
Collect
Ingest raw data from any source. Web, enterprise systems, sensors, or proprietary repositories.
02
Curate
Filter, deduplicate, and score data quality. Identify gaps and priority slices for annotation.
03
Annotate
Human and AI-assisted labeling with multi-stage QA. Every label verified against ground truth.
04
Deliver
Versioned, split-ready datasets exported to your training infrastructure with full lineage.
Ready to build better training data?
Talk to our data team about your pipeline requirements.