Enables auditing of extracted balance sheets, income statements, and cash flow reports by reconstructing complex document tables and verifying mathematical consistency and formula balancing. It also performs cross-reference resolution and sensitive PII redaction, returning structured matrices and validation telemetry over MCP to clients like Claude Desktop, Cursor, and Windsurf.
Enables AI agents and MCP-compliant clients to extract visual data from charts and graphs into structured markdown tables using deterministic, zero-dependency Python.
Enables AI assistants to autonomously digitize graphs and tables in academic papers and technical documents entirely offline, extracting PDF primitives, detecting axes and ticks, running OCR, pulling plot pixels by color, calibrating to real-world values as CSV, and verifying results with overlay images. Runs fully locally with no external API calls or model downloads, and also exposes OpenAI Function Calling schemas and a safe dispatcher.
Enables deterministic reconstruction of complex tables from OCR bounding-box payloads, recovering multi-column layouts, merged headers, and grid hierarchies. It also supports formula consistency auditing and sensitive PII redaction for document processing pipelines.
Audits extracted balance sheets, income statements, and cash flow reports for mathematical consistency and formula balancing, reconstructing complex financial tables and resolving cross-references from document or OCR payloads. Also performs spatial layout parsing and PII redaction, returning structured matrices and validation telemetry over MCP.