Regoxa Data extraction and validation
Accurate, reliable data extraction that powers better decisions
Instant access to the data your processes run on
01
Any document, any language, any level of complexity
Mercury handles structured documents like tax forms, semi-structured ones like invoices, and unstructured ones like agreements, across many languages. It pulls business-critical data from multi-page documents and complex tables, keeping your workflows smooth and automated.
02
No templates, no training
Because the LLM reads and understands the document, you skip the slow setup that template- and rule-based tools demand. Upload a document and Mercury identifies the domain, bank, invoice, finance, and more, then extracts what matters.
03
Tables and complex layouts
Multi-page tables, mixed layouts, and cluttered documents come back as structured data, with the relationships between fields preserved.
04
Handwriting and tough inputs
Mercury reads handwritten data, cursive included, along with checkboxes, barcodes, and digital signatures, opening automation up to document types that traditionally needed manual handling.
05
Built-in normalization and validation
Extracted data is normalized and validated automatically, with cross-checks, sum checks, vendor matching, and purchase order validation. Discrepancies get flagged for review, and you can tune the rules to fit your business.
06
Reliable, verifiable output
Mercury returns what's actually on the page, so your workflows and downstream models run on clean, trustworthy inputs.
07
Put LLMs to work safely
LLMs open up real possibilities, but raw, general-purpose models can invent results. Mercury is purpose-built for extraction, so you get the upside, reading and understanding documents in context, with verifiable output you can rely on for business-critical work.
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What is data extraction, and why does it matter?
Data extraction pulls specific details, names, dates, amounts, and other crucial information, out of documents and turns them into a format your automation can use. It's what makes business process automation possible in the first place.
What types of data can be extracted from documents?
Mercury can handle even complex, unstructured documents, picking up information from text, tables, handwritten notes, checkboxes, barcodes, and digital signatures.
Can I integrate the extracted data with my existing systems?
Yes. Mercury delivers data into business process management (BPM), enterprise content management (ECM), enterprise resource planning (ERP), robotic process automation (RPA), and other systems through connectors or the Regoxa REST API, so you can act on it right away, for instance pushing invoice details straight into your accounting system with no manual entry.
How accurate is the extraction, and is the data validated?
Mercury extracts data accurately and validates it with built-in checks so the output meets your criteria. For critical processes or tricky documents, a person can review and confirm the result before it moves on.
Can I train my own custom models with Regoxa IDP?
Yes. You can build and customize document processing skills for your specific needs, and the low-code platform means you can start deploying custom models with little effort. Configure and train models for any document type in any language, or grab a ready-made skill from the Regoxa Marketplace and uptrain it on your own documents.
What types of documents can Regoxa IDP handle?
A wide variety, including invoices, purchase orders, contracts, forms, receipts, and more. Its flexible architecture processes both digital and paper documents and handles structured, semi-structured, and unstructured formats efficiently.
What are the security features of Regoxa Document AI?
It includes strong protections across the document processing lifecycle, such as data encryption, access controls, and secure connections, in line with industry standards and regulations, so confidential information stays guarded against unauthorized access.
How does Regoxa Document AI improve data accuracy?
It uses advanced machine learning to extract and validate information, and it keeps learning over time, adapting to new patterns and correction feedback to reduce errors and inconsistencies in capture and processing.
Frequently asked questions
Get to business-critical data, fast and accurately
Data extraction sits at the heart of the intelligent document processing (IDP) pipeline. Mercury uses an LLM to read a document, work out what it is, and pull the data out, including tables, automating data capture and driving real efficiency.
There's no template to build and no per-document-type model to train first. Mercury identifies the document's domain on its own and extracts the relevant fields, so processing is faster and more accurate from the first upload.