medical AI, and autonomous systems. Real-world data built for your specific use case and architecture."/>
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Solutions

Data for Every
AI Use Case

Different AI applications need fundamentally different training data. Here is what we provide for each major use case.

Use Case / 01
Computer Vision Models
Annotated image and video data from real environments. Not stock photos, not synthetic scenes.
  • Object detection, segmentation, and depth estimation
  • Rare class and edge-case augmentation
  • Multi-condition coverage (lighting, weather, scale)
  • Industry-specific scene types on demand
// Annotation schema
{"image_id":"IMG_2847",
"scene":"urban",
"objects":[{
"class":"pedestrian",
"confidence":0.97,
"occluded":false
}],
"weather":"overcast"}
Use Case / 02
Large Language Models
Pre-training corpora, instruction-tuning pairs, RLHF preference data, and domain-specialist text.
  • Multilingual instruction-response pairs (80+ languages)
  • Chain-of-thought reasoning traces with corrections
  • Domain specialist corpora: legal, medical, scientific
  • Preference pairs for RLHF and constitutional AI
// Instruction pair
instruction:"Explain ACE inhibitor
mechanism in hypertension"

response:[verified by MD]
domain:"medical"
language:"en"
consent:verified
Use Case / 03
Medical AI
Annotated by licensed clinicians, de-identified to HIPAA standards, and traceable to source. Zero tolerance for data quality errors.
  • Radiology: X-ray, CT, MRI with diagnostic labels
  • Pathology slide images with expert annotations
  • Clinical NLP: de-identified EHR text and notes
  • Genomics: variant annotations and phenotype data
// Medical record
modality:"chest_xray"
findings:[{
"region":"right_lower",
"finding":"consolidation",
"annotator":"radiologist_md"
}]
hipaa_cleared:true
Use Case / 04
Autonomous Systems
Real-world multi-sensor data from actual operational deployments, not closed test circuits.
  • LIDAR, camera, and radar synchronized captures
  • HD maps with semantic lane and surface labels
  • Edge cases: construction, unusual obstacles, weather
  • Multi-agent scenarios with trajectory annotations
// Sensor frame
timestamp:1708234847.223
lidar_pts:124850
cameras:[front,left,right]
objects_3d:[
{"type":"car","dist":14.2}
]
city:"tokyo"

What Does Your Model Need?

Tell us your use case. We will tell you what data properties will move your metrics.

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FAQ

Solutions Questions

How do I know which dataset type is right for my model?
We start every engagement with a technical conversation with your ML team. We look at your model architecture, your current training data, your evaluation metrics, and where performance is falling short. From there we identify which data properties — diversity, domain coverage, annotation quality, class balance — are most likely to move your specific metrics. In many cases teams come in thinking they need more data, and what they actually need is different data. We help identify which.
Do you work with teams that are early in their AI development?
Yes. We work with teams at every stage — from pre-seed startups building their first model to frontier labs with established training pipelines. For earlier-stage teams, we often recommend starting with a focused, high-quality dataset for a specific capability rather than a broad dataset. Getting your model to work well on a narrow, well-defined task first is more efficient than training broadly on poor data from the start.