Data Extraction Tool

The tool ingests unstructured content and applies field-level extraction rules to produce structured records that align with a predefined schema. It supports source_text, source_html, and source_url inputs, plus a flexible ruleset where each field specifies a name, data_type, pattern or NLP hint, and whether it is required. Conceptually, extraction hinges on anchored patterns and optional entity-type hints, followed by type coercion and normalization to canonical formats. Beneficiaries include data engineers, analysts, and researchers who need repeatable, auditable extraction for invoices, contracts, forms, and web content. Core value lies in consistent schema output, batch processing, and easy export to JSON, CSV, or YAML for integration with analytics platforms and databases. Core features include field mapping, per-field validation, and error metadata; advanced capabilities cover regex templates, confidence scoring, and multi-document aggregation. Use cases span invoice parsing (numbers, dates, totals), contract data capture (parties, effective dates), and product catalogs (SKU, price, availability).
Users can define extraction_rules once and reuse them across datasets, improving reproducibility and governance. The tool emphasizes data quality with validation rules, normalization, and explicit handling of missing or ambiguous matches, while remaining agnostic to front-end presentation. It is suitable for teams seeking scalable, auditable data capture from diverse textual sources without resorting to custom scraping code.