Ember
High-precision ingestion that converts scientific literature into clean, queryable data with full provenance — built for semantic search, analytics, and LLM workflows.
Simulated demo. Responses include inline citations and source links.
Overview
Ember continuously parses PDFs and supplemental data from scientific papers, normalizes units, deduplicates records, and preserves line-level citations. Every number is traceable back to its table/figure/page — so you can trust the output and build reliable search & analytics on top.
Key Features
Intelligent PDF Parsing
Segment tables, figures, captions, and body text. Reconstruct complex layouts and capture relationships between entities, units, and context.
Unit Normalization
Auto-detect and convert measurements (e.g., GPa, MPa·m1/2, °C, W/m·K) to consistent canonical units with metadata preserved.
Deduplication
Merge near-duplicate facts while keeping unique context. Confidence scores prevent over-eager merges.
Full Provenance
Every datum links back to {doc → section → table/figure → page}. Perfect for audits, citations, and regulatory requirements.
Ready to get started?
Let’s map your literature sources and boot a private Ember pipeline for your team.