The Data Engine for AI Science
ImageQuant transforms unstructured scientific literature into high-fidelity, machine-readable datasets. Powering the next generation of foundation models for materials science and biology.
The Data Bottleneck
Trapped in PDFs
Millions of valuable scientific images are locked inside PDF figures, inaccessible to machine learning models.
Missing Metadata
Scraped images often lack critical experimental context, scale bars, and imaging parameters.
Unstructured
No standardized formats make it impossible to build large-scale foundation models for science.
The ImageQuant Engine
An end-to-end infrastructure for converting scientific papers into structured, ML-ready datasets at scale.
Ingestion
Automated crawling of open-access repositories (arXiv, PubMed) with PDF parsing.
Neural Extraction
VLMs identify, crop, and categorize figures while extracting captions and scale bars.
Expert Validation
PhD-level domain experts review every single image to guarantee ground truth.
Model-Ready Data
Delivery of clean, schema-validated JSON/Parquet datasets ready for training.
High-Fidelity Data Extraction
Explore our v1 dataset containing over 50,000 materials science images. Each data point includes full provenance, JSON metadata, and semantic tags.
Sample Dataset
Extracted Tags
{
"figure_number": "2",
"caption": "Figure 2. SEM images of sample surfaces ablated at a fixed point with different numbers of laser pulses...",
"page": 4,
"is_relevant": true,
"modality": "SEM"
}Built on Data Provenance
Every data point in ImageQuant is traceable to its original scientific publication. We maintain strict lineage tracking to ensure data quality and licensing compliance.
Ready to train your next foundation model?
Accelerate your research with high-fidelity scientific datasets. Contact us to see our platform in action or discuss your custom data needs.
Trusted by research labs and AI teams worldwide.