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Data Extractor provides a programmable pathway to convert unstructured sources into structured outputs. It is designed for developers, data engineers, analysts, and researchers who require repeatable, auditable data capture from invoices, contracts, emails, logs, and reports.
Conceptually, the tool ingests text or documents, applies a configurable extraction schema, and emits structured data. Two core modes exist: rule-based extraction using labels, patterns, and normalization; and machine learning-backed named-entity recognition with optional custom models. Users can blend approaches to maximize coverage and accuracy.
Inputs include source text or documents, extraction_schema, language, and output_format. Outputs include structured_data, per_field_confidence, and provenance metadata. The tool supports batch processing, language localization, and multiple export formats (JSON, CSV, YAML) to fit data pipelines.
Common use cases include pulling vendor names and totals from invoices, extracting dates and IDs from contracts, parsing product codes from catalogs, and converting log events into structured records for analytics.