Which document types does it recognise?

Invoices, purchase orders, receipts and delivery notes. A document that fits none of them is marked uncategorized.

98% field-level accuracy, measured on 100 documents and 373 fields. We compared each extracted value by hand with the correct one. The set covered invoices, purchase orders, agreements and shipping documents.

Does anyone check the results?

Yes. A person reviews each result before it is exported.

[✓] LIVE DEMOAI Document Intelligence

SwiftDocs: Receipt and Invoice Data Extraction

SwiftDocs automates invoice and receipt data entry. Upload a PDF or image and it extracts every field on the page, categorizes the document as an invoice, purchase order, receipt or delivery note, and exports structured data, at 98% field-level accuracy.

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The problem

Invoices, receipts and other documents arrive as PDFs and scans, and someone has to retype their fields into another system by hand. It is slow, and every single retyped field is a chance for a mistake.

How it works

Upload a document
Upload a PDF, TIFF, PNG, JPG or JPEG file, and the system then reads it and begins extracting its fields.
Fields are extracted
An OCR and LLM pipeline reads the fields and labels it as an invoice, purchase order, receipt or delivery note.
Review, then export
A person reviews each result before export, and every processed document stays searchable in the history view.

Screenshots

Walkthrough

About one minute

Sign in. The demo login page shows the demo credentials below the form, ready to type in.
On Upload, choose a file from your computer; the sidebar lists the document types it knows.
The file joins the queue and is processed, and the result comes back labelled as an invoice.
Select Review to see the document beside its fields, each with an editable confidence score.
Export the reviewed result as an Excel or CSV file, ready for use in your other business systems.
The History page lists every processed document with its type, field count and confidence.
Filter History by vendor, type or date, such as Receipt or Delivery Note, and export the list.
The Dashboard shows documents extracted, accuracy, fields found and speed, plus a time chart.

Results

98%field-level accuracy

An OCR plus LLM pipeline reads each file. Accuracy covers 373 fields in 100 hand-checked documents: invoices, purchase orders, agreements and shipping documents, from our own and public samples.

Built with

Next.js
OCR pipeline
LLM extraction
structured export

Common questions

01
Which file formats does it accept?
PDF, TIFF, PNG, JPG and JPEG.
02
Which document types does it recognise?
Invoices, purchase orders, receipts and delivery notes. A document that fits none of them is marked uncategorized.
03
How accurate is it?
98% field-level accuracy, measured on 100 documents and 373 fields. We compared each extracted value by hand with the correct one. The set covered invoices, purchase orders, agreements and shipping documents.
04
Does anyone check the results?
Yes. A person reviews each result before it is exported.

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SwiftDocs: Receipt and Invoice Data Extraction

The problem

How it works

Upload a document

Fields are extracted

Review, then export

Screenshots

Walkthrough

Results

Built with

Common questions

Which file formats does it accept?

Which document types does it recognise?

How accurate is it?

Does anyone check the results?

Need something similar?

SwiftDocs: Receipt and Invoice Data Extraction

The problem

How it works

Upload a document

Fields are extracted

Review, then export

Screenshots

Walkthrough

Results

Built with

Common questions

Which file formats does it accept?

Which document types does it recognise?

How accurate is it?

Does anyone check the results?

Need something similar?