PDF data extraction

Automatically extract clean, structured data from any PDF — whether it's a paragraph of text, a multi-page table, or a complex form. Our engine intelligently interprets document layout and hierarchy to ensure nothing gets lost in translation.

Powered by advanced Optical Character Recognition and deep learning, our system reads both native and scanned PDFs with remarkable accuracy. It detects visual layout elements — like columns, headers, and labels — to maintain the document's true structure.

Built with a developer-first approach, our API is easy to integrate into your existing software stack. With clear documentation, SDKs, and fast support, your team can go from concept to deployment in record time.

Need to process thousands of PDFs at once? No problem. Our batch processing engine is optimized for performance and scalability, allowing you to handle large document volumes efficiently with parallel processing and queue management.

We take data security seriously. All data is encrypted in transit and at rest, with strict access controls and logging. Our platform is built to meet industry standards like GDPR, HIPAA, and SOC 2, giving you peace of mind and regulatory compliance.

Not all documents are digital-native — many come in as image-based scans. Our solution can handle low-quality or complex image PDFs, transforming them into usable, searchable, and structured data without manual effort.

Data point customization allows the user to specify exactly what information they want from a PDF, such as invoice numbers, customer names, due dates, or product codes. Instead of extracting all text blindly, the system targets relevant patterns or fields, making the output cleaner and more actionable.

This feature ensures that when extracting data, the spatial arrangement and formatting of the PDF content are preserved. This is important for documents like contracts, reports, or complex forms where layout communicates meaning.

You can implement this by parsing the document into logical text blocks and using text proximity analysis, or by applying natural language processing (NLP) to detect the "key" (label) and its associated "value."

Automation eliminates repetitive manual uploads by allowing scheduled or trigger-based extractions. For instance, PDFs dropped into a specific folder could be automatically processed and the extracted data sent to a database.

Integration ensures that extracted data flows seamlessly into other systems like CRMs, ERPs, spreadsheets, or APIs without manual copying and pasting. This can be done using REST API connectors, webhooks, or middleware platforms.

Even the most powerful extraction system is useless if the interface is confusing. A simple, intuitive UI allows non-technical users to select files, configure extractions, and preview results without coding.