Live throughput and status updates.
Basic metadata only scratches the surface. Finding the right research requires deeper insight.
Every paper search loop costs hours. Abstracts mislead. Details hide inside PDFs. Scale is impossible.
the bottleneckTitles and abstracts don't reveal methodology, datasets, or experimental setup.
Datasets used, evaluation metrics, baselines, and limitations are buried deep in PDFs.
Institutional affiliations and research topic taxonomies are inconsistent or absent entirely.
Reading dozens of papers to extract comparable data points is slow and error-prone.
Three steps from paper identifier to structured data — predictable JSON schema designed for automation
arXiv ID, DOI, or PDF URL posted to the ingest endpoint
PDF download + arXiv metadata API: authors, dates, categories, source files
PDF → text, section boundary detection, figure & table isolation
Parallel prompts: entities, methods, metrics, affiliations, topics, paper type
JSON schema enforcement, confidence scoring, dedup against existing index
Full-text + vector index. Queryable via REST in <100 ms
Jump straight to the experiment setup, datasets used, metrics reported, and limitations—without reading entire PDFs.
{
"uid": "2505.20959",
"title": "Research Community Perspectives...",
"category": ["survey"],
"tags": [
"Natural Language Processing (NLP)",
"Research Survey",
"Intelligence Criteria"
],
"affiliations": [
{
"author": "Anna Rogers",
"institution": "IT University..."
}
],
"abstract": "Despite the widespread...",
"released": "2025-05-27T09:53:27"
}
From literature reviews to building research intelligence products
Jump directly to experiments, methods, and results across dozens of papers. Compare approaches and metrics side-by-side in minutes.
Feed your LLM structured sections with proper context. Build reliable paper-intelligence products on predictable schemas.
Track affiliations, labs, and research topics as they emerge. Map institutional output and influence over time.
Compare approaches, datasets, and evaluation metrics reliably across papers. Identify gaps and opportunities.
Find papers using specific datasets, metrics, or evaluation methods. Match prior work to your experimental setup.
Create pipelines on stable structured fields. Scale your research infrastructure without fragile PDF parsing.
We're onboarding researchers, developers, and teams building on structured research data. Get in touch to discuss your use case.
Request AccessWe continuously index new papers from arXiv and other sources. Most papers are processed within 24-48 hours of publication.
Currently we support arXiv papers. Support for DOI-based papers and direct PDF uploads is coming soon.
We use state-of-the-art LLMs with carefully designed prompts and validation. Accuracy varies by paper structure, but we provide confidence scores and are continuously improving extraction quality.