Everything you need to turn unstructured documents into structured data—without templates, training data, or manual configuration.
Upload any document — PDF, scan, or photo — and get structured data back immediately. No setup, no templates, no waiting.
The AI reads document structure by context, not coordinates. New document layouts work on the first upload without any setup or template creation.
Define custom extraction rules in plain English. Tell the AI what to extract—“find the payment terms” or “extract the lot number”—and it applies across all documents.
Every extracted field includes a confidence score. Set thresholds to automate high-confidence results and route uncertain fields for review.
Set up a dedicated email address. Documents that arrive as attachments or in the email body are processed automatically without manual upload.
Export to Excel, Google Sheets, CSV, JSON, or XML. One-click download or direct push to connected spreadsheets.
Programmatic access with structured JSON responses, field-level confidence scores, and webhook notifications for async processing.
The fundamental limitation of template-based data extraction is maintenance. Every new document layout requires someone to define zones, map fields, and test the configuration. For organizations processing documents from dozens or hundreds of sources, the template library becomes a project in itself.
Layout-agnostic extraction eliminates this overhead entirely. The AI understands what an invoice number is, where a total typically appears, and how line items are structured—regardless of the specific document format. This means a new vendor, a reformatted bank statement, or an unfamiliar receipt format works immediately.
For teams that have been maintaining template libraries, the transition to layout-agnostic extraction removes an ongoing operational cost. For teams just starting with data extraction, it means they can process real documents within minutes of signing up, without a setup phase. Lido’s bulk extraction engine is built on this layout-agnostic approach, so there is no template creation step—upload a document and the AI handles the rest.
Template-based tools require manual configuration for each document layout, defining zones where specific fields appear. When a vendor changes their invoice format or a new document type is introduced, someone must create or update the template. Layout-agnostic extractors use AI to understand document structure by context and meaning, so any document format works on the first upload. Lido’s extraction engine does not require templates, training data, or per-format configuration.
AI columns let users define custom extraction rules in plain English. Instead of mapping coordinates or writing regex patterns, you describe what you want extracted in natural language. For example, you might create an AI column that says “extract the payment terms from this invoice” or “find the lot number on this packing slip.” The AI interprets the instruction and applies it across all documents. This is especially useful for extracting fields that are unique to a specific business or industry.
Confidence scoring assigns a probability to each extracted field, indicating how certain the AI is about the result. A confidence score of 0.98 means the AI is highly confident, while a score of 0.72 might indicate ambiguity due to poor scan quality or unusual formatting. Teams use confidence thresholds to automate review workflows, sending only low-confidence extractions to human reviewers while high-confidence results flow through automatically.
Modern AI extractors can process handwritten text and low-quality scans, though accuracy depends on legibility. The AI uses context clues from the document structure to improve interpretation of unclear characters. Confidence scoring is particularly valuable here, as it flags uncertain extractions for human review rather than producing silent errors. Lido handles handwritten documents and provides confidence scores so teams know exactly which fields need verification.
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