Sensible.so

Analyzes the visual structure and layout of documents to understand spatial relationships between fields, tables, and text blocks. Uses machine learning to identify document regions and their semantic meaning without requiring manual field mapping.

Provides an intuitive visual interface for creating document extraction templates without writing code. Users can click on document regions to define what data to extract and how to structure the output.

Validates extracted data against defined rules and quality standards, flagging inconsistencies, missing fields, and data quality issues. Provides confidence scores and enables manual review workflows.

Automatically normalizes and formats extracted data into consistent structures, handling variations in date formats, currency symbols, phone numbers, and other standardized fields. Converts raw extracted text into clean, usable data.

Integrates with downstream systems and workflows through APIs, webhooks, and pre-built connectors. Enables automatic routing of extracted data to databases, CRMs, accounting systems, and other business applications.

Automatically classifies documents by type and routes them to appropriate extraction templates or workflows. Identifies document categories (invoice, contract, form, etc.) and applies the correct processing logic.

Maintains detailed audit trails of all extraction activities, including who accessed documents, what data was extracted, when changes were made, and confidence scores. Supports compliance requirements and regulatory audits.

Provides comprehensive REST and programmatic APIs for developers to integrate document extraction into custom workflows, applications, and data pipelines. Enables full control over extraction logic, validation, and post-processing.

Handles extraction from documents spanning multiple pages, maintaining context and relationships between data across page boundaries. Automatically segments and processes each page while preserving document-level structure.

Automatically adapts to documents with inconsistent layouts, formatting variations, and structural differences. Learns from examples to identify the same logical data even when its position or appearance changes between documents.

Recognizes and extracts data from handwritten fields in documents, including signatures, annotations, and handwritten form entries. Uses OCR and machine learning to interpret cursive and print handwriting.

Identifies and extracts data from tables, matrices, and other structured data layouts within documents. Preserves table structure and relationships between rows and columns.

Provides ready-made extraction templates for common document types in finance, legal, and insurance domains. Includes templates for invoices, tax forms, loan agreements, insurance claims, and other standard documents.

Delivers extraction accuracy above 95% on structured and semi-structured documents through advanced ML models and minimal training data requirements. Optimized for production-grade accuracy without extensive manual training.

Processes multiple documents in batch mode, automatically applying extraction templates to large volumes of files. Enables efficient processing of hundreds or thousands of documents without manual intervention.