Problem Statement
Raw scientific data is scattered across diverse formats and inconsistent structures, making it hard to retrieve and standardize manually.
Our Solution
Our Data Extraction system automates:
- Fetching relevant documents using smart queries
- Parsing different formats using specialized pipelines (PDF, OCR, HTML, etc.)
- Structuring data into consistent schemas
Key Features
- 🧾 Format-Agnostic Parsing – Extract from HTML, PDF, CSV, image-based documents
- 🧠 NLP + ML-Based Extraction – Understand context to capture relevant values
- ⚙️ Configurable Pipelines – Tailor extraction logic to your dataset type
- 📚 Metadata Capture – Log source, publication date, and reliability flags
Workflow Overview
- Fetch sources using the fetcher engine
- Parse content based on file type
- Identify relevant material properties
- Store structured data in database
