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Extraction Errors: What to Do When the AI Reads a Field Incorrectly

Learn how to diagnose and resolve the most common extraction errors in Dost: incorrect VAT IDs, wrong date formats, misread amounts or VAT, using a step-by-step decision tree.

Extraction Errors: What to Do When the AI Reads a Field Incorrectly

In this section, you will learn how to act when Dost’s AI incorrectly extracts a specific field: supplier VAT ID, invoice date, amounts, VAT types, invoice number, units, discounts, and more.

Understanding which tool to use in each case helps you resolve issues in a structured way instead of repeatedly fixing the same errors manually.

Why Can the AI Misread a Field?

Dost’s AI performs its best interpretation of the document, but errors may occur in situations such as:

  • The supplier uses an unusual invoice format (complex tables, multi-page layouts, special characters).
  • The field appears in an unexpected position or non-standard format (for example, dates in Catalan or uncommon formats).
  • The document is a low-quality scan or image.
  • The supplier has recently changed their invoice layout.
  • It is a new supplier with insufficient training history.

Decision Tree: Which Tool Should You Use?

When you detect a misread field, follow this decision flow:

Step 1 – Is it a one-time error or recurring for this supplier?

  • One-time error (first occurrence or atypical document):
    Correct the field manually and mark the document as Reviewed. No further action is needed.
  • Recurring error for the same supplier:
    Proceed to step 2.

Step 2 – Is it a reading error or a business rule issue?

  • Reading error (AI misinterprets the value):
    Use self-training (see Self-Training section). The goal is to teach the AI to correctly read the field for that supplier.
  • Business rule transformation (value is correct but needs adjustment):
    Use a mapping (see Mappings section) to transform or complete the value according to your rules.

Step 3 – Is it a format-related issue (date, currency, decimals)?

Common Cases and How to Fix Them

VAT ID (CIF/NIF) Not Detected or Incorrect

Common cause: VAT ID appears in an unusual position or includes variations (e.g. dashes, “ES” prefix).

Recommended solution:

💡 If the supplier includes the “ES” prefix and this causes issues, you can create a mapping to remove it or transform it into the format required by your ERP.

Invoice Date Read in Incorrect Format

Common cause: Supplier uses a different date format (MM/DD/YYYY instead of DD/MM/YYYY) or writes dates in text form (e.g. “15 March 2026” or Catalan format).

Recommended solution:

  • For a specific supplier: adjust Format Settings in document configuration.
  • For textual or language-based dates: use self-training.
  • If it is a global issue: update default format in Format Settings.

Amounts, VAT, or Net Values Incorrect

Common cause: Complex table structure, multiple VAT rates, or non-standard invoice layouts.

Recommended solution:

  • Correct values manually.
  • Use self-training if the issue is recurring.
  • If calculations are required (e.g. deriving net amount from total and VAT), use a mapping (see mapping use cases).

Invoice Number Misread

Common cause: Invoice number appears in a non-standard location or is mixed with other identifiers.

Recommended solution:

  • Correct manually.
  • Apply self-training.
  • If the issue persists, contact Dost support with supplier VAT ID and a sample invoice.

What If Training Does Not Improve Results?

If self-training does not resolve the issue after several documents:

  • Verify that training examples are representative and not edge cases or corrupted formats.
  • Check in the Self-Training section that training status is Completed (see Training Status article).
  • Consider whether a mapping is more appropriate than training.
  • If the issue persists, contact support with:
    • Supplier VAT ID
    • Failing field
    • Expected vs actual behavior
    • At least one representative invoice example

Best Practices

  • Do not correct the same error manually more than 2–3 times without applying a corrective action (self-training or mapping). Manual effort will exceed setup effort over time.
  • When you detect a new error pattern, document it internally: supplier, field, and applied solution. This improves long-term maintainability and onboarding of new team members.
  • Self-training and mappings are complementary: you can train the AI to read data correctly and also use mappings to transform it according to business rules.