Your Inbox Is Full of Data Nobody Can Use
Purchase orders, contact-form submissions, lead notifications from providers, shipping confirmations — machine-generated email arrives all day in a format only a human can read. So a human reads it, and retypes it into a system.
An email parser pulls the fields straight out of those messages. Once parsed, the data comes back in a format that's actually easy to handle — CSV, Excel, XML or JSON — ready to download, or to push automatically into the apps that need it.
Any Machine-Generated Email
Purchase orders, contact forms, lead-provider notifications — if it's structured, it can be parsed.
No Retyping, No Typos
The fields land in your system exactly as they were sent — humans never touch them.
Acts the Moment It Arrives
A lead is in your CRM seconds after it lands — not after someone gets round to the inbox.
Parse It, Then Put It Somewhere Useful
Extraction is half the job. Delivery is the other half.
Easy XML Configuration
Define what to pull and from where in a simple XML config — no rebuild needed when a template changes.
Microsoft 365, Exchange & IMAP
Connect to the mailbox you already run — Microsoft 365 and Exchange Online through the Microsoft Graph API, or any IMAP/POP mailbox.
CSV, Excel, XML or JSON
Get parsed data back in whichever format the receiving system actually wants.
Straight Into Your Database
Skip the file entirely — dump parsed records directly into your database as they arrive.
Auto-Update Other Apps
Use the parsed output to keep your CRM, ERP or order system current without a person in the loop.
Retrieve the Source Email
Pull the original emails behind any parsed record — so you can always trace a value back to where it came from.
Templates, Machine Learning or Both
There are two broad ways to pull data out of email. Template or rule-based parsing looks for fixed labels and positions — the text after Order Number, the third row of a table — and works extremely well when the sender's format never changes. It is fast, cheap to run and fully predictable, but it breaks quietly when a supplier redesigns their email.
Machine-learning and large-language-model extraction reads the message more like a person would, so it copes with free-text enquiries and senders who all format things differently. The trade-off is that it can be confidently wrong, costs more per message and needs its output checked. In practice the most reliable setups combine the two: rules for the high-volume, consistent senders, model-based extraction for the long tail, and validation on everything.
What Separates a Parser You Can Trust
Extraction is the visible part. These are the parts that decide whether the data is safe to act on.
Validation Before Delivery
Each field can be checked against rules before it is passed on — a date must be a real date, a total must equal the sum of its lines, a customer code must exist in your system. Records that fail go to a review queue instead of silently landing in your database.
Measuring Accuracy
Before going live, run a batch of real historical emails through the parser and compare the output with what a person would have entered. Track accuracy per field and per sender, not as one overall figure, so a weak spot on one supplier is not hidden by the rest.
Mailbox Integration
For Microsoft 365 and Exchange Online the parser connects through the Microsoft Graph API with modern OAuth sign-in rather than a stored password, and other providers are read over IMAP or POP. A dedicated mailbox or forwarding rule keeps the parser away from personal inboxes.
Data Privacy
Emails often carry personal data, so decide what is kept and for how long. A parser can store only the fields you need, purge source messages on a schedule and restrict who can view originals. If an external AI model is used, check where the data is processed and whether the provider retains it.
Choosing the Right Kind of Parser
Hosted parsing services are a quick start when you have a few simple email types and are comfortable with messages passing through a third party. A custom parser is worth considering when volumes are high, when attachments such as PDF orders matter as much as the email body, when data must stay on your own infrastructure, or when the output has to feed an in-house system with its own validation rules.
The setup process is straightforward: collect a representative sample of real emails, agree the fields and the destination for each, build and test the extraction, then run it alongside the manual process until the results match. Plan for change too — senders redesign their emails, so someone should be alerted when a familiar format suddenly stops parsing. With PixoBots, you own the code and IP, and an NDA is signed before any sample emails are shared.
Get the Data Out of Your Inbox
Send us a sample email and the fields you need, and we'll show you the parsed output.