Process hundreds of PDFs, scans, and photos in a single batch—every layout, no templates, no pre-sorting. 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.
Drag in whole folders of PDFs, images, or scans. Connect cloud drives, email inboxes, or your own application via the REST API—batches flow in automatically.
The engine reads every document in the batch contextually, pulling structured fields from invoices, reports, contracts, forms, and any other layout without templates or pre-sorting.
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. One batch, first upload, zero configuration—every file came back structured.”
“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
Bulk data extraction is the processing of documents at volume—hundreds of vendor invoices at month-end close, a lending file of bank statements, a year of receipts for an audit, an archive of contracts during due diligence. The defining constraint is not any single document but the pile: work that is trivial for one file becomes a staffing problem at five hundred.
Manual entry fails at volume in a predictable way. Throughput per person is roughly fixed, so capacity is added by adding people, and error rates climb as the work becomes more repetitive. Teams respond with double-keying and spot checks, which consume still more hours. The cost of the pile grows linearly while the deadline—a close, a filing, a funding decision—stays fixed.
Template-based automation was the first answer, and it works when a batch is uniform: five hundred copies of the same form can be processed with one zone configuration. But real piles are mixed. A month of invoices spans hundreds of vendor layouts, and a lending file mixes statements from dozens of banks. Template systems force someone to pre-sort the batch and maintain a configuration per layout, which quietly reintroduces the manual labor the tool was meant to remove.
Layout-agnostic AI extraction changes the economics because the batch no longer needs to be sorted at all. The model reads each document contextually, identifying fields by meaning rather than position, so mixed layouts flow through a single pipeline. Field-level confidence scoring completes the picture: instead of re-checking everything, reviewers work a short exception queue of flagged fields while the clean majority passes straight through.
Lido is built for exactly this workflow: drag in a folder, forward an inbox, or sync a drive, and every document in the batch comes back as structured rows with confidence scores—no templates, no training data, no pre-sorting. 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 the bulk extraction engine 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.
Bulk data extraction is the automated processing of large volumes of documents—hundreds or thousands of PDFs, scans, or photos—into structured data in a single workflow. Instead of handling files one at a time, documents are uploaded or routed in as a batch, AI extracts the target fields from each one, and the combined results land in a spreadsheet, CSV, or API feed. Lido processes batches with the same layout-agnostic engine no matter how many formats the batch contains.
Route the documents into the extractor in bulk: drag in multiple files at once, forward them by email, sync a cloud drive folder, or push them through an API. The AI processes every file without per-document setup, and results consolidate into one structured output. Lido supports all of these intake paths and appends each processed document as new rows in the same sheet, so the batch arrives ready for review or export.
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.
Yes, if the extractor is layout-agnostic. Template-based tools require batches to be pre-sorted by format so the right template can be applied to each group, which reintroduces the manual work the batch was meant to eliminate. AI extraction identifies fields by meaning rather than position, so a single batch can mix invoices from hundreds of vendors or statements from different banks. Lido reads each file in the batch contextually with no sorting or setup.
Robust bulk extraction does not silently guess. Each extracted field carries a confidence score, and low-confidence fields or unreadable pages are flagged for human review while the rest of the batch proceeds. This turns quality control from a full re-check of every document into a short exception queue. Lido provides field-level confidence scores so teams review only the flagged items instead of spot-checking the entire output.
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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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