Upload a PDF, scan, or photo and get clean, structured data back in seconds—no templates, no setup. Built on Lido’s AI extraction engine.
Upload any document — PDF, scan, or photo — and get structured data back immediately. No setup, no templates, no waiting.
Upload PDFs, images, or scans directly. Connect cloud drives, email inboxes, or your own application via the REST API—documents flow in automatically.
The app reads each document contextually, pulling structured fields from invoices, reports, contracts, forms, and any other layout without templates.
Route structured output to Excel, Google Sheets, databases, or downstream systems via API. Set up once and every new document is processed automatically.
“We process invoices from over 200 vendors with completely different layouts. The app handled them all on the first upload without any configuration.”
“Manual data entry was eating 15 hours a week. We cut that to under an hour by letting the AI extract everything into a spreadsheet automatically.”
“The confidence scoring is what sold us. We set a 95% threshold and only review flagged fields instead of spot-checking everything.”
Audited controls over a sustained period, not a point-in-time check.
Bank-grade encryption at rest and TLS 1.2+ in transit.
Documents deleted within 24 hours. No copies retained.
Last updated: August 2026
Ask ten teams how they extract data from documents and you will hear some version of four workflows. The most common is still manual: open the PDF on one monitor, retype the numbers into a spreadsheet on the other. It requires no tooling, but it scales linearly with volume—double the documents, double the hours—and transcription accuracy drops as fatigue sets in.
The second workflow pairs a generic OCR converter with manual cleanup. The converter turns the document into raw text or a rough spreadsheet, and someone reshapes the output by hand—fixing merged columns, re-pairing labels with values, deleting page furniture. It feels faster than retyping until the cleanup time is actually measured; for complex layouts it routinely exceeds the time it saved.
The third is template-based extraction. Software is configured with zones for each known layout: the invoice total lives in this region, the date in that one. Templates work when documents come from one or two consistent sources, but each new vendor, bank, or form revision demands another configuration, and the template library becomes its own maintenance burden.
The fourth—and the reason this category has changed in the last few years—is layout-agnostic AI extraction. The model reads each document contextually, the way a person does, identifying fields by meaning rather than position. New layouts work on the first upload, which removes the setup step entirely rather than merely making it faster.
Lido takes this fourth approach. Upload a PDF, scan, or photo and the AI returns structured data with field-level confidence scores on the first try—no templates, no training data, no cleanup pass. For a detailed look at why template-based approaches break down at scale, see The Problem with Template-Based Document Extraction on the Lido blog.
Explore how ExtractData handles your specific needs: review the full feature set for extraction capabilities, see available integrations with downstream systems, and browse use cases across industries and document types.
The fastest way is an AI extraction app: upload the document, let the AI read it, and export structured data. Retyping scales with volume, generic converters produce output that needs manual cleanup, and template tools require configuration before the first document can be processed. AI extraction works on the first upload with no setup. Lido reads PDFs, scans, and photos in seconds and returns clean rows for Excel, Google Sheets, CSV, or JSON.
AI data extractors handle a wide range of document types including invoices, receipts, purchase orders, bank statements, financial reports, medical forms, bills of lading, packing slips, contracts, and tax documents. The key advantage over template-based tools is that the same extraction engine works across all document types without separate configurations. Lido processes PDFs, scanned images, photographs, and digital documents with the same layout-agnostic approach.
AI-based data extraction typically achieves 95 to 99 percent accuracy on well-structured documents, which matches or exceeds manual data entry accuracy. The advantage is consistency: AI does not experience fatigue or make transcription errors that increase with volume. For edge cases like handwritten text or damaged scans, confidence scoring flags uncertain fields for human review rather than guessing silently. Lido provides confidence scores on every extracted field so teams can set review thresholds appropriate for their accuracy requirements.
No. Template-based tools require a zone configuration for every document layout before extraction can begin, and model-trained systems need labeled training data for each new document category. A layout-agnostic app reads each document contextually on the first upload. Lido requires no templates and no training data, and AI columns let you describe any custom field you want extracted in plain English.
Template-based extractors require a separate configuration for each document layout, which becomes unmanageable when processing documents from many different sources. Layout-agnostic extractors use AI models that understand document structure contextually, identifying fields by their semantic meaning rather than fixed coordinates. This means a new vendor invoice format, a differently structured bank statement, or an unfamiliar medical form works on the first document without any setup. Lido uses this layout-agnostic approach and adds AI columns that let users define custom extraction rules in plain English.
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Built on Lido’s OCR engine
Built on Lido’s OCR engine
Built on Lido’s OCR engine
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