Email data extraction: how to extract data from emails and attachments automatically

Email data extraction is pulling specific fields out of the emails your team receives, from the message itself and from its attachments, and turning them into structured data another system can use. In most operations teams the valuable data sits in the attachments: invoices in the accounts payable inbox, certificates of insurance in the vendor compliance inbox, bank statements and pay stubs in the loan processing inbox. Automating it means software splits each email into its documents, reads and checks the fields, and sends only the doubtful ones to a person.

This guide covers what to extract, how the process works, the problems shared inboxes cause, and how to set it up.

What data can you extract from an email?#

Each part of an email holds different data, and each needs a different method.

• Header fields

  Sender, recipients, date, subject and message ID. They come as text with every email and record who sent what, and when.

• Body text

  Order numbers, amounts, dates and names written in the message, sometimes as a table. It's already text, so it needs parsing, not OCR.

• Attachments

  Invoices, purchase orders, certificates of insurance, bank statements, pay stubs and bills of lading. Each document type has its own fields.

• Scans and photos

  Image-only PDFs, phone photos and screenshots. They need OCR before any field can be read: this is the part people call email OCR.

Here's one attachment, an invoice, read field by field:

How email data extraction works#

Most automated setups follow the same path. Mail lands in a shared inbox, each attachment is split out and identified, its fields are extracted and checked, and a person sees only the exceptions before the data reaches your system.

Keep the email's sender, date and message ID with every document you split out. The message ID is unique to each email, so it ties each record to the email it came from and shows when the same email has been processed twice.

Manual vs automated inbox processing#

Put in your own inbox's numbers:

Common challenges with shared inboxes#

Reading the text is rarely what breaks email automation. These are the checks to build in:

• Mixed attachmentsOne PDF can hold several invoices and a statement, and one email can carry a ZIP file. Split and classify each document before extracting anything.
• Logos and signature imagesEmail signatures add logos as small inline images. Filter them out so a logo isn't processed as a document.
• DuplicatesVendors resend invoices as reminders, and replies can carry the same attachment again. Compare the invoice number, vendor and amount, not the file name: see duplicate invoice detection.
• Reply threadsQuoted text repeats values from earlier messages. Read the newest message, and use the reply headers to tie a thread together.
• Junk and off-topic mailNewsletters, auto-replies and spam land in the same inbox. Filter them out before extraction so they don't create empty records.
• SecurityAttachments carry personal and bank data, and shared inboxes attract phishing and spoofed invoices. Limit who can open the inbox, and confirm any change of bank details by calling the vendor on a number you already have.

How to set up automated email data extraction#

1. Start with one inboxPick the busiest one, often accounts payable, and sort a week of its mail: which document types arrive, from whom, and how many.
2. Get the mail to the extractorUse a forwarding rule, or pull messages and attachments through your mail provider's API, such as the Gmail API or Microsoft Graph, and send each file to the extraction tool's API.
3. Classify and extractSplit PDFs that hold several documents, identify each one, and extract its fields with a model trained for that document type.
4. Validate, then reviewCheck totals, dates and IDs against your records, and send fields below a confidence threshold to a person, with the source on the page highlighted.
5. Send the data onPost clean records to your AP, policy or loan system by API or webhook, each carrying the message ID of the email it came from.

Docsumo handles steps 2 to 4. It takes documents in by email through its own inbox, so a forwarding rule is enough to get them there. It has pre-trained models for 250+ document types, including invoices, ACORD forms and bank statements, and reads handwritten as well as printed text. On the Business plan it classifies and splits files automatically and checks values against your own records (master data lookup). You set a confidence threshold for each field, and when a reviewer clicks a field, its source line is highlighted in the document. For accounts payable, it also flags duplicate invoices and, on the Business plan, routes invoices for approval (accounts payable automation). Results go out through its API and webhooks or as an Excel export. It runs in the cloud only.

• 99%99%field-level accuracy across 250+ document types
• 95%+95%+of documents processed straight through, without manual review
• 99%+99%+of invoices processed touchless at Valtatech

The bottom line#

The data worth extracting from a shared inbox is mostly in the attachments. Start with one inbox, split and classify what arrives, extract and check the fields, and let people handle only the exceptions.

Book a demo with a few of the documents your inbox receives, or start a free trial.

Frequently asked questions#