Lido alternatives in 2026 for PDF & invoice data extraction

Disclosure: StatementDecoder is ours and appears below. Lido popularised the no-template idea — upload a file, say which fields you want, get rows back. Here's where that shines and where a different tool fits.

What Lido does well

No workflow to build: define the data you need and export structured rows across many document types — invoices, receipts, statements, bills of lading — in any language or currency, straight to CSV or a sheet. It's a fast path from "pile of PDFs" to "usable table."

Where to look elsewhere

• You want the numbers checked. Defining columns gets you the values; it doesn't confirm an invoice's subtotal, tax and total actually reconcile.
• You want Markdown for AI. Feeding documents to an LLM is better as clean Markdown than as extracted cells.
• You're mostly doing bank statements. Statement work rewards merchant naming, categories and balance reconciliation — a different specialism.

Alternatives, by job

Verify-first invoices and receipts

StatementDecoder extracts the fields and checks that line items → subtotal → tax → total add up, flagging mismatches on screen — and it never fabricates a total to make the maths work. Free to preview, files never stored.

PDF → Markdown for ChatGPT / RAG

If the destination is an LLM, convert to Markdown instead of cells: PDF → Markdown for ChatGPT keeps tables and headings intact so the model reads structure, not noise.

Enterprise scale / trainable model

For team volume with approvals and ERP hooks, Nanonets or Rossum go further — see Nanonets alternatives.

Recurring documents from known senders

Docparser / Parseur for rules-based intake — see Docparser alternatives.

The short version

• Stay on Lido for quick, no-template field extraction across mixed documents.
• StatementDecoder when the totals must be verified, or you need PDF → Markdown, or you're doing bank statements.
• Nanonets / Rossum for enterprise IDP.
• Docparser / Parseur for predictable, rules-based intake.