Data Normalization and Validation

Same shape, clean data for every document

From mailbox schemas to post-processing, every extracted value lands clean, validated, and ready for downstream systems.

What's included

Mailbox-level schemas

A consistent schema is what makes downstream integrations and automations actually reliable. Define your fields once and every document the mailbox processes maps to the same shape.

Standard fields for single values, table fields for repeating data
Plain-English instructions tell the AI what to capture for each field
Adjust fields anytime through the UI, or programmatically via the API

Field-level formatting

Built-in formats normalize dates, numbers, addresses, and more. The right format is inferred from document context, with mailbox-level defaults as fallback.

Dates parse any order, separator, or month name across languages
Numbers parse any decimal/thousands separator across regional formats
Address fields geolocate and split addresses into structured parts

Data validation

Every extracted result is verified against the mailbox schema. Failures surface in the UI, trigger an email notification, and fire a webhook, so both ops teams and tools hear about them.

Schema check confirms the AI result matches the field shape
Required-field check catches missing values at the source
Choice-field check flags values outside the allowed list

Post-processing rules

When standard formatting and validation aren't enough, drop in a small Python script. Rules run after extraction to reshape values or run custom validation against your business logic.

Combine, split, or compute new fields from extracted values
Apply business logic, lookups, or conditional transforms
Available on Pro plan and above

How Data Normalization works

What just happened

Multi-Engine Document Parsing

Vision AI, Text AI, templates, or OCR pulled structured fields from each document.

Learn more

Map to schema

Extracted values are mapped to the fixed set of fields defined for the mailbox. Every document, no matter the source layout, ends up with the same column shape on output.

Mailbox fields

Text Vendor Acme Corp

Text Invoice # INV-0142

Date Issued on 2026-05-07

Number Total 2840

Table Items 3 cols, 2 rows

Item Qty Price Consulting 12 $200 Equipment 2 $220

Format

Each field runs through its configured format. Dates and numbers normalize across regional variations using document context, names split into first/middle/last, addresses parse into structured parts.

Date May 7, 2026 2026-05-07

Number $1,234.56 1234.56

Address 742 Evergreen Ter, Springfield 62704

742 Evergreen Terrace Springfield IL 62704 USA

Validate

Each result runs through the validation checks before moving on. Documents that pass continue to post-processing, the rest are flagged so nothing leaves Parseur unnoticed.

Validation

Vendor Acme Corp

Issued on 2026-04-15

Total Required missing

Status rejected

Allowed: open paid closed

Post-process

Optional Python rules run last, applying business logic that field-level formatting can't express. Combine fields, look up reference data, or shape output to match an exact downstream contract.

post_process.py

def post_process(data):

if data["Total"] > 1000:

data["Shipping"] = "express"

else:

data["Shipping"] = "standard"

return data

Number Total 2840

Text Shipping express

What happens next

Real-time Exports and Integrations

Normalized data is delivered to your CRM, accounting system, or database in real time.

Learn more

Back to all features

Clean data, ready for your systems.

Define the fields you need, pick the formats that fit, and watch every extraction land in the right shape.

Free plan included, no credit card needed

Process your first document in under 2 minutes

Cancel anytime, no commitment

Frequently Asked Questions

Common questions about Parseur's normalization and validation, from date and number formats to validation rules and Python post-processing.

Data normalization is the step that turns raw extracted values into clean, consistently shaped data. Dates from different documents land in the same format, numbers parse correctly across regional conventions, addresses split into structured parts, and every field maps to a fixed schema, so downstream systems always receive the same shape.

Parseur's Date field parses any order, separator, or month name across languages, and uses document context to disambiguate ambiguous values like 03/04/2026. Output is normalized to a consistent format so your downstream system always receives the same shape.

Yes. The Full name format splits names into first, middle, and last parts. The Address format geolocates and splits addresses into structured components. Both run automatically once the field format is set.

Yes. Every result is checked against the mailbox schema, required-field rules catch missing values, and choice-field rules flag values outside the allowed list. Failures surface in the UI, send an email notification, and fire a webhook so both ops teams and your tooling hear about them.

Yes. Post-processing rules let you drop in a small Python script that runs after extraction and standard validation. Use it to combine, split, or compute new fields from extracted values, apply business logic, run lookups, or shape output to match an exact downstream contract. Available on the Pro plan and above.

Without normalization, every document leaves a slightly different output: dates in different orders, numbers with different separators, names and addresses jumbled into single strings. Downstream tools end up rejecting rows or storing inconsistent data. Normalization fixes that at the source so integrations actually stay reliable.

The Number field parses any decimal and thousands separator across regional formats, including European 1.234,56 and US 1,234.56 conventions, Indian lakh and crore grouping like 1,00,00,000, and accounting notation where parentheses indicate negatives like ($123,456,789.12). The right format is inferred from document context, with mailbox-level defaults as fallback.

Parseur supports Text, Date, Time, Datetime, Number, Full name, Address, and Choice field formats. Each format carries its own parsing and validation rules, and standard fields capture single values while table fields capture repeating data row by row.

The document's status is set to Process Failed rather than being silently exported, and an email notification goes out. If a process-failed webhook is configured, that fires too. You can review and fix the document manually, or wire failures into your own monitoring.

Each mailbox carries its own schema and every document the mailbox processes maps to the same fixed set of fields. So a single mailbox can ingest invoices from many different vendors, with many different layouts, and still output the same column shape for every row.