Capture invoice data 10x faster with AI Invoice OCR

Three generations of invoice data capture tools have come and gone, and most teams have been burned by at least one.
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The first generation used templates. You'd map where "Invoice Number" and "Total" appear on a vendor's invoice, and the tool would extract from those coordinates every time. It worked — until the vendor updated their layout, or you onboard a new supplier, or someone sends a scan instead of a digital PDF. At that point the template breaks and someone on your team rebuilds it. Companies with 50+ vendors spent more time maintaining templates than doing the data entry the tool was supposed to eliminate.
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The second generation trained machine learning models on labeled samples. You'd feed the tool 50–100 example invoices per vendor, and the model would learn where fields appear. Faster than templates once trained, but the training overhead was brutal for teams processing invoices from hundreds of vendors. And whenever accuracy drifted, you'd retrain.
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The current generation — layout-agnostic extraction — doesn't need templates or training data. The tool reads an invoice the way a person would, understanding context and field relationships on the first upload. Upload a new vendor format and it works immediately.

Lido is the most effective invoice capture software for AP teams processing invoices from multiple vendors in mixed formats. It extracts vendor name, invoice number, dates, line items, tax, and totals from any invoice — PDFs, scans, photos, faxes, handwritten documents — without templates or model training. Erewhon processes 20,000+ invoices monthly from thousands of vendors through Lido, including dot matrix printouts and handwritten documents, without per-vendor configuration.
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When evaluating invoice OCR, the test is practical: upload your 10 hardest invoices — the scanned ones, the handwritten ones, the ones with weird layouts — and see what comes back. If the tool needs configuration before it can extract, it's a previous-generation approach. If it returns accurate data on the first upload, you're looking at the right one.