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Open the selected PDF or image without modifying it.
Active prototypeLocal OCRPrivacy first
Business document processing
Docusoak
Turn incoming documents into reviewable, structured work—without sending sensitive business data off the machine.
Docusoak is a Windows-first application for identifying order numbers and other metadata, proposing safe filenames, and separating high-confidence results from documents that need a human decision.
Interactive preview
Inspect a document locally
A limited browser demonstration of evidence-based extraction. Your file is processed in this tab and is never uploaded.
● LOCAL BROWSER MODE
Drop a document here
Text-based PDF, TXT or CSV · 5 pages · 5 MB
Review result
Waiting for documentDetected fields, confidence, and supporting evidence will appear here.
Preview disclaimer
This interactive preview is an illustrative JavaScript demonstration, not a browser version of Docusoak and not a representation of the desktop software's full accuracy or workflow. The product uses a separate Python-based local processing architecture with OCR, review, and document-control capabilities that cannot be reproduced fully here. Do not rely on preview results for business processing.
Request more information →Preview boundary: no OCR, handwriting recognition, batch processing, renaming, filing, persistence, or export. Your selected document remains in this browser tab and is not uploaded.
The problem
Document automation becomes dangerous when uncertainty is hidden.
Business documents are inconsistent. Scans can be faint, handwriting can be ambiguous, and a plausible-looking order number can still be wrong. Blind automation turns those recognition errors into misnamed or misfiled records.
Docusoak treats confidence and provenance as part of the result. The system should automate the routine cases, show the evidence behind a decision, and stop safely when the evidence is not strong enough.
Processing path
A deterministic, reviewable pipeline
Recognise
Render pages locally and run CPU-first OCR.
Extract
Locate order numbers and structured metadata with supporting evidence.
Validate
Apply business rules and assess confidence before proposing an action.
Review
Keep ambiguous results visible for a person to resolve.
Local by default
Document bytes, page images, extracted text, and business metadata stay on the Windows machine. There is no cloud OCR fallback.
Evidence over guesses
Extracted values retain their source evidence and confidence. Uncertain order numbers are sent to review instead of being silently accepted.
Safe test boundaries
The test pipeline can read, render, recognise, extract, and validate, but it has no filing dependency and cannot mutate source documents.
Replaceable components
OCR, extraction, validation, confidence, persistence, and filing sit behind narrow interfaces so each can evolve independently.
Technical foundation
Built for dependable local operation
The prototype uses Python 3.12, RapidOCR, PDFium rendering, and ONNX Runtime. CPU processing is the baseline; optional DirectML acceleration is selected only when a benchmark proves it faster on that machine.
// current system boundary
PDF / PNG / JPG input
↓ local rendering + OCR
Evidence-bearing metadata
↓ validation + confidence
Read-only result / human review
Current status: active prototype
Local PDF inspection, manual test splitting, OCR, runtime detection, and order-number extraction are being developed behind a safety-first architecture. Automatic document boundaries, production filing, persistence, and the complete GUI remain staged work.