ScantoExcel.co has launched a new AI-powered platform that converts scanned documents into structured Excel files. The software is designed to help businesses reduce manual data entry by extracting tabular data from paper records, scanned files, and document images without template setup.
Maryland, United States, 1st Apr 2026 – ScantoExcel.co recently announced the launch of its document processing software built to convert scanned documents into structured Excel outputs using AI OCR.
The platform was developed for businesses and teams that still rely on spreadsheets to manage information originally captured in paper documents, scanned records, and image-based files. In many organizations, this work is still handled through manual entry or traditional OCR systems that require fixed templates and repeated adjustments. ScantoExcel.co is intended to offer a more flexible approach by reading both text and table structures directly from scanned inputs and transforming them into spreadsheet-ready data.
The software supports a range of common business records, including invoices, receipts, faxed documents, and other scanned materials that need to be transferred into Excel for review, reporting, or downstream processing. According to the company, the system can work with files produced by scanners, multifunction printers, and mobile devices, including documents that lack an embedded text layer.
A central focus of the launch is reducing the operational burden created by document-heavy workflows. Many finance, operations, and administrative teams continue to spend significant time rekeying information from scanned files into spreadsheets, particularly when dealing with older records or inconsistent layouts. ScantoExcel.co is positioned as a tool for organizations seeking to move those processes into a more automated workflow without requiring per-document configuration.
The company also stated that the software was designed to perform under conditions that often make scanned documents difficult to process, such as faded print, skewed pages, low-resolution scans, and uneven lighting. By addressing these common quality issues at the extraction stage, the platform aims to make digitization more practical for businesses working with real-world documents rather than ideal source files.
ScantoExcel.co said the platform includes security controls intended for organizations handling sensitive information. The company states that it is SOC 2 Type 2 certified, uses encryption for data in transit and at rest, and does not use customer files to train AI models. Processed documents are automatically deleted within 24 hours.
The launch comes as more businesses look for practical ways to connect legacy paper records with modern spreadsheet-based workflows. Rather than asking teams to redesign documents around software limitations, ScantoExcel.co is entering the market with a product built to adapt to the format and condition of the documents businesses already have.
About ScantoExcel.co
https://www.scantoexcel.co specializes in using AI OCR to convert scanned documents into structured Excel files. The company focuses on helping organizations extract usable spreadsheet data from image-based documents without templates or manual setup.
Media Contact
Organization: ScantoExcel.co
Contact Person: Ella Murphy
Website: https://www.scantoexcel.co/
Email: Send Email
State: Maryland
Country:United States
Release id:43451
The post ScantoExcel.co Launches New AI Platform for Converting Scans to Excel appeared first on King Newswire. This content is provided by a third-party source.. King Newswire makes no warranties or representations in connection with it. King Newswire is a press release distribution agency and does not endorse or verify the claims made in this release. If you have any complaints or copyright concerns related to this article, please contact the company listed in the ‘Media Contact’ section
Disclaimer: The views, suggestions, and opinions expressed here are the sole responsibility of the experts. No Science Currents journalist was involved in the writing and production of this article.