Parse PDFs, scans, and photos into structured data—no rules to write, no templates to maintain. Built on Lido’s AI parsing engine.
Upload any document — PDF, scan, or photo — and get structured data back immediately. No setup, no templates, no waiting.
Upload PDFs, scanned images, or photos. Set up a watched folder or email inbox so new documents are parsed the moment they arrive.
The parser understands document hierarchy—section headers, multi-column tables, key-value pairs, and nested line items are all extracted with labeled confidence scores.
Export to Excel, CSV, or JSON. Use the REST API to stream parsed data into databases, ERPs, or any system that consumes structured input.
“We receive documents from 300+ suppliers in completely different formats. The smart parser handled every layout without a single rule or template.”
“Our team was spending 20 hours a week keying in data from shipping manifests. Automated doc parsing cut that to a quick review each morning.”
“The API-first approach was exactly what we needed. We built a pipeline that parses incoming contracts and pushes key terms into our database automatically.”
Audited controls over a sustained period, not a point-in-time check.
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Documents deleted within 24 hours. No copies retained.
Last updated: August 2026
A smart parser is a document parser that works out the structure of each file on its own. Instead of executing rules that a developer wrote for one specific layout, it reads the page the way a person does—recognizing headers, tables, labels, and values by their meaning and their relationships to each other. The practical consequence: the parser does not need to be told where anything is before it can extract it.
The contrast is with the two generations of parsers most teams have already tried. Rule-based parsers locate fields with regex patterns and positional logic—invoice number at line 3, column 40. They are easy to fool: a sender adjusts their format, the rule points at the wrong text, and the error surfaces only after bad data has flowed downstream. Every new document source means more rules, and every rule is a small standing liability.
Template parsers replaced code with configuration: an administrator draws zones on a sample document, and the parser reads those zones on every similar file. This lowered the skill barrier but kept the core constraint—one layout, one template. Teams parsing documents from dozens or hundreds of senders end up curating template libraries that demand constant attention as formats drift.
Machine-learning parsers generalized further by training statistical models on labeled document sets, and they do absorb minor variation. But they carry their own tax: each new document category needs labeled training data, and accuracy drops on layouts far from the training distribution. Collecting, annotating, and retraining is a recurring cost that grows with document diversity.
A smart parser removes the setup step entirely. The AI interprets each document contextually—a value beneath a “Total” label is a total wherever it sits and whatever font it uses—so the first document from a new sender parses as reliably as the thousandth. Lido takes this approach and pairs it with field-level confidence scores, so uncertain values are flagged for review instead of passed through silently. For more on why template-based approaches struggle with document diversity, see The Problem with Template-Based Document Extraction on the Lido blog.
Explore how the smart parser fits your workflow: see how automation connects parsing to your downstream systems, review the full feature set for extraction and output options, and browse use cases across industries and document types.
A smart parser understands documents by context instead of following fixed rules. Rule-based and template parsers extract text from predefined positions and break when a layout changes. A smart parser uses AI vision models to recognize what each value means, identifying an invoice total or a due date by its label and surroundings rather than its coordinates. Lido applies this contextual approach so any document format works on the first upload, with no rules to write and no templates to draw.
AI-based smart parsers handle PDFs, scanned images, photographs, Word documents, and digital files. The parser reads the visual structure of each page rather than relying on embedded text layers, which means it works equally well on native PDFs and scanned paper documents. Lido supports PDF, JPEG, PNG, TIFF, and other common file formats with the same parsing engine.
Rule-based parsers depend on regex patterns and positional logic written for one specific layout, so every new sender or format revision requires new rules, and existing rules break silently when documents drift. A smart parser needs no layout-specific logic: the AI reads each document contextually, which means documents from hundreds of different sources flow through the same pipeline. Lido processes new layouts on the first upload and flags uncertain fields with confidence scores instead of failing silently.
Yes, table and line item extraction is a core capability of modern smart parsers. The AI identifies table boundaries, column headers, and individual row data even when tables lack visible gridlines or use inconsistent spacing. This is critical for invoices with line items, purchase orders with product lists, and financial statements with transaction tables. Lido extracts multi-row tables and maps each column to the correct field automatically.
Smart parsers typically output to Excel spreadsheets, Google Sheets, CSV for system imports, JSON for API integrations, and XML for legacy platforms. The parsed data maintains its field structure across all formats, so an invoice total stays labeled as a total whether exported to Excel or returned via API. Lido supports all of these output formats and provides a REST API that returns structured JSON with confidence scores on every field.
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Built on Lido’s parsing engine
Built on Lido’s parsing engine
Built on Lido’s parsing engine
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