How to Extract Document Layout Data and Send It to Any Target

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Workflow Name:

Extract Document Layout and Send It to Any Target
AI Model Type:

Vision / Layout LLM
Model Provider:

Goldfinch AI / OpenAI
Task Type:

Layout Detection & Segmentation
Input Type:

PDF / Image
Output Format:

JSON / XML
Who Uses It:

Document AI Teams; Data Engineers
Category:

Description

Problem Before:

Unstructured document layouts
AI Solution:

AI-based layout segmentation
Validation (HITL):

Sampled layout review
Accuracy Metric:

Block detection accuracy %
Time Savings:

80% faster preprocessing
Cost Impact:

Lower manual tagging cost

Extract Document Layout and Send It to Any Target

This workflow performs Document Layout Extraction from PDFs and images using vision-based layout models.

Structured Layout Intelligence for Downstream Systems

The system detects and segments document elements such as headers, tables, paragraphs, and sections, then converts the layout structure into JSON or XML. It enables document AI teams and data engineers to preserve document structure, improve downstream parsing, and accelerate intelligent document processing pipelines.

Watch Demo

Video Title:

Pharma Industry Document Automation: Use Cases, Data Flow |AI Document Understanding
Duration:

13:18

Outcome & Benefits

Accuracy:

96%
Touchless Rate:

85%
Time Saved:

From 3m to 30s/page
Cost Saved:

$0.20 per page

Functional Details

Business Tasks:

Document preprocessing
KPI Improved:

Parsing success rate
Scheduling:

Batch / Real-time
Downstream Use:

Datalake / Doc AI Pipelines

Technical Details

Model Name/Version:

GPT-4o-mini Vision
Hosting Type:

Cloud API
Prompt Strategy:

Layout-aware prompting
Guardrails:

Schema validation
Throughput:

120 pages/min
Latency:

~1s/page
Data Governance:

No content retention

FAQ

1. What is the Extract Document Layout and Send It to Any Target workflow?

It is an AI-powered workflow that detects, segments, and structures document layouts using vision and layout-aware LLMs, then sends the layout data to any target system.

2. How does the workflow work?

The workflow ingests documents in PDF or image format, applies vision and layout LLM models to identify sections, tables, headers, paragraphs, and forms, and exports the structured layout data to the configured target.

3. What layout elements can be detected?

It can detect and segment layout elements such as titles, headings, paragraphs, tables, columns, forms, images, footers, and page structure metadata.

4. What AI models are used in this workflow?

The workflow uses vision and layout-aware LLM models provided by Goldfinch AI and OpenAI to accurately understand and segment complex document layouts.

5. What is the output of the workflow?

The extracted layout structure is output in JSON or XML format and can be sent to downstream systems such as Document AI pipelines, search indexing engines, Datalakes, or content management systems.

6. Who uses this workflow?

Document AI Teams and Data Engineers use this workflow to build downstream extraction pipelines, improve document understanding, and standardize layout-aware processing.

7. What are the benefits of automating document layout extraction?

Automation enables accurate layout understanding, improves downstream extraction quality, supports complex document types, and accelerates scalable document AI workflows.

Case Study

Industry:

Legal / Enterprise Docs
Problem:

Poor layout visibility
Solution:

AI layout extraction
Outcome:

Better downstream extraction
ROI:

2-month payback

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