High-quality data supply — faster & cheaper.
High-quality data supply — faster & cheaper.
We source the best medical and healthcare professionals, thoroughly vet and assess them, and can staff them on your platform or ours at no extra cost. We then use proprietary AI models to pre-label or synthetically generate your data, always backed by a multi-human review process to ensure accuracy.
Expert Labeling
A specialist won’t do it. The Specialist will.
High-quality labeled datasets are essential for training reliable models, but sourcing accurate, expert-reviewed data is slow, expensive, and inconsistent.
We provide access to rigorously screened specialists from leading universities and hospitals, evaluated across dozens of key characteristics to ensure gold-standard quality and consistency.
[Example]
Sourced 237 radiology doctors with 5+ years of experience working in top10 medical facilities in the US.
Customizable Pipelines
Standard pipelines don’t fit your unique solution.
Standardized labeling workflows don’t fit the complexities of AI in healthcare. Without flexible pipelines, scaling high-quality data collection is inefficient.
We build bespoke labeling workflows for any healthcare AI use case.
[Example]
We made the dataset PII-compliant, created a digital twin, and segmented it for precise labeling by specialized doctors. After review, each data point was sent directly back into the model (DPO).
Pre-Labeled Data with Teacher Models
Human data is slow and expensive.
We blend human and AI intelligence to boost expert efficiency—faster data creation with fewer errors. This allows us to offer better price while spending more time on ensuring quality.
[Example]
Created a model-only pipeline to bring down the cost per label from $1.3 to $0.45 while increasing the weekly throughput from 6000 tasks to 38540 tasks.
High-Quality Synthetic Data
Augmented labels don’t often lead to performance boost.
We leverage our expert network at both ends of the pipeline, generating high-quality data that overcomes biases while significantly expanding dataset size.
[Example]
We collaborated with doctors to identify key factors influencing skin cancer risk, analyzed characteristic distributions, and synthetically generated variants beyond the initial distribution, expanding the dataset from 15K to 75K images. This improved performance by 65% compared to standard augmentation.
Fast Turnaround / <5 minutes per task
Lack of training data.
It’s often hard to get access to real world data. One way to tackle this is through fast-turnaround projects. We guarantee that files are reviewed by professionals in minutes or hours—not weeks or months. This allows you to launch your product, collect real-world data, and get it labeled simultaneously.
[Example]
A required dataset wasn’t available, so the client launched an app powered by our workforce with guaranteed turnaround, ensuring a great user experience. This enabled data collection and labelling in real-time to train their model.
How to Start
- Intro Call — Understand your challenges and goals.
- Scoping Stage — Deep dive into your use case.
- Quick Hit Test — Validate quality & pricing with a small dataset.
- Sign-Off — Align on terms, timelines, and expectations.
- Pilot Run — Scale the pipeline with continuous feedback.
- Full Deployment — Expand into production with measurable improvements.