Data for the model you're building.
Robots, voice assistants, document readers and language models all need data that isn't on the web. Here's what we collect and label for each one, and how we check it.
Physical AI and robotics
Robots learn from demonstrations, sensor data and feedback that only exist in the physical world.
Humanoids · logistics · industrialCollect
- Egocentric video in staged homes and workplaces
- Teleoperation and multi-device capture
- LiDAR and RGB-D in real environments
Label
- 3D cuboids, segmentation and object state
- 6-DoF hand and object pose, grasp points and trajectories
Evaluate
- Expert review of robot behavior
- Safety ranking and failure detection
- Corrective demonstrations for retraining
Speech and voice AI
Voice products fail on the accents, rooms and phrasing they never heard in training.
Voice assistants · contact centersCollect
- Scripted and spontaneous speech across languages
- Accents, dialects, noisy rooms and far-field audio
Label
- Transcription, timestamps and speaker turns
- Intent and entity labels
- Translation and cultural context
Evaluate
- Word error rate and quality audits
- Review by native speakers
Vision and multimodal
Perception and vision-language models need images and video that match where they will be deployed.
Perception · VLMs · mediaCollect
- Image and video sourcing, with the source of every file recorded
- Targeted capture to fill gaps in your data
Label
- Boxes, polygons and segmentation
- Object tracking across frames
- Captions and video-language pairs
Evaluate
- Label quality audits
- Model error analysis and evaluation sets
LLMs and reasoning
Language models now improve most from expert judgment on hard prompts.
Foundation model labs · copilotsCollect
- Domain-expert prompts and responses
- Reasoning traces and critiques
- Multilingual instruction data
Label
- Preference ranking for RLHF
- Factuality checks against sources
- Safety and policy labels
Evaluate
- Rubric-based scoring
- Red-teaming
- Benchmark creation
Healthcare and documents
Document models have to read the real forms, receipts and reports your users send, in their formats and languages.
Document AI · health techCollect
- Real forms, receipts and reports, collected by region
- Captured on different phones and scanners, in different lighting
Label
- Field extraction and OCR transcription
- Layout and table structure
- Document classification
Evaluate
- Field-level accuracy audits
- De-identification checks
- Per-locale quality review
Localization
A product launching in a new market needs data from the people who live there.
Global platforms · consumer AICollect
- Contributors in each target market
- Regional speech, text and imagery
Label
- Cultural context labeling
- Translation review
- Sensitive-content labeling
Evaluate
- Bias and fairness review
- Locale-specific quality checks
What you can hire us for.
Training data
Custom and licensed datasets for pre-training, fine-tuning and RLHF.
Evaluation
Model performance measured on accuracy, reliability and real-world fit, with domain-specific metrics.
Reasoning
Multi-step problems, rationales and critiques that teach models to work through a task.
Factuality
Expert verification of model outputs against sources, to catch and reduce hallucinations.
Prompt creation
Prompt sets that combine text, images, audio and video to test and train multimodal models.
Benchmarking
Repeatable test sets and scoring so you can compare models and track progress.