No-Code

Enrichments

Extract custom data from documents using AI—no coding required

What are Enrichments?

Enrichments are AI-powered data extraction fields that you configure in plain language. Instead of manually reading thousands of documents, you define what you're looking for once, and George AI extracts it automatically from all documents.

Enrichment fields are added to Lists (custom views of your Library files). Each field defines a piece of information to extract, and George AI processes all documents in the List to populate that field.

How It Works

Define Field

Name + AI prompt
AI Processes

Extracts from all docs
Review Data

Table view + export

Real-World Example: Pharmaceutical Packaging

A pharmaceutical company needed to extract specifications from 30,000+ packaging PDFs from design agencies.

Enrichment Field	What It Extracts	Example Result
SAP Product ID	10-digit product code	4012345678
Printing Colors	Color specifications	Pantone 1234, CMYK
Package Dimensions	Width × height × depth in mm	150×200×30 mm
Market Languages	Target market codes	EN, DE, FR

Result: All 30,000 documents processed automatically. Data exported to SAP for product management.

Creating an Enrichment Field

Navigate to List Settings

Open the List where you want to add an enrichment field

Click List Settings → Fields

Click "Add Field" button

Configure Required Settings

Field Name	Required • 2-100 characters What you want to extract (e.g., "Product Code", "Invoice Amount")
Data Type	Required string text number date datetime boolean Choose the type that matches your data. Use text for long content, string for short values.
AI Model	Required Select which AI model to use for extraction. Available models depend on your AI Services configuration.
AI Prompt	Required • 10-2000 characters Describe in plain language what to extract. Be specific about format, location, and variations. `Example: "Extract the SAP product identification` `number. It's usually a 10-digit code starting` `with '40' or '50', found in the top-right corner` `of the first page."`

Configure Optional Settings

Failure Terms

Comma-separated terms that indicate extraction failure or missing data

Example: "not found, N/A, missing, unknown"

If the AI returns any of these terms, the enrichment will be marked as failed.

Vector Store Search

Enable semantic search to find relevant document chunks before extraction

Use Vector Store

When enabled, provide a Content Query (2-100 characters) describing what content to search for.

This helps the AI focus on relevant sections of large documents, improving accuracy and speed.

Context Fields

Select other enrichment fields to provide context to the AI

Example: When extracting "Unit Price", include

"Currency" as context so AI knows the price format

Context fields are shown to the AI along with the document, helping it make better extraction decisions.

Test and Apply

After creating the field, test it on a few documents before processing the entire List:

Testing Workflow

Filter your List to show 5-10 representative documents
Click the field header → Start Enrichment
Review extracted values in the table
If results are inaccurate, edit the field and adjust the AI prompt
Once satisfied, remove filters and run enrichment on the entire List

Enrichment Queue

Track processing progress in Admin Panel → Enrichment Queue. Monitor success rates and troubleshoot failures.

Tips for Writing Effective Prompts

✓ Do This

• Be specific about format ("10-digit number starting with 40")
• Mention typical location ("top right of first page")
• Provide examples ("like 4012345678 or 5087654321")
• Describe variations ("may have dashes or spaces")
• Specify units if applicable ("in millimeters", "in EUR")

✗ Avoid This

• Vague descriptions ("find the code")
• Conflicting requirements ("must be text and number")
• Too many things at once (split into multiple fields)
• Ambiguous language ("the main ID"—which one?)
• Assuming document structure (not all docs are the same)

Managing Enrichment Fields

Start Enrichment

Process all documents in the List (or filtered subset) to populate the field. Only missing values are enriched by default.

Stop Enrichment

Cancel all pending and processing tasks for this field. Already completed enrichments remain.

Clean Enrichments

Clear all cached enrichment values for this field. Use this to re-extract data after updating the prompt.

Editing Fields

You can edit enrichment fields at any time. After editing, use Clean Enrichments to clear old values, then Start Enrichment to re-process with the new prompt.

Field Types

Type	Description	Use Cases
`string`	Short text values (IDs, codes, names)	Product codes, customer IDs, status values
`text`	Long text content (descriptions, notes)	Product descriptions, comments, summaries
`number`	Numeric values (integers or decimals)	Prices, quantities, dimensions, percentages
`date`	Date without time (YYYY-MM-DD)	Expiration dates, manufacturing dates
`datetime`	Date with time (ISO 8601 format)	Order timestamps, delivery times
`boolean`	True/false values	Compliance flags, approval status, availability

Monitoring Enrichment Progress

Track enrichment processing in real-time:

Field Header Status

3 processing

12 pending • Click header for controls