How to Extract Add-On Documents and Send Data to Any Target
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| Workflow Name: |
Extract Add-on Document and Send It to Any Target |
|---|---|
| AI Model Type: |
LLM / Vision |
| Model Provider: |
Goldfinch AI / OpenAI |
| Task Type: |
Extraction / Structuring |
| Input Type: |
PDF / Image |
| Output Format: |
JSON / CSV |
| Who Uses It: |
Operations; Finance Teams |
Table of Contents
Description
| Problem Before: |
Manual data entry from add-on docs |
|---|---|
| AI Solution: |
OCR + LLM structured extraction |
| Validation (HITL): |
Sampled QA review |
| Accuracy Metric: |
Field-level accuracy % |
| Time Savings: |
80% reduction in processing time |
| Cost Impact: |
Lower operational effort |
Extract Add-on Document and Send It to Any Target
This workflow enables Document Extraction from add-on documents by processing PDFs or images using LLM and vision models to capture structured data accurately.
Automated Data Capture and Delivery
The system analyzes uploaded documents, extracts relevant fields, structures the output in JSON or CSV format, and sends the results to any configured target system. It helps operations and finance teams reduce manual data entry, improve accuracy, and accelerate downstream processing.
Watch Demo
| Video Title: |
How to Overcome Supplier & Fulfilment Challenges? – D2C No-Inventory Model Explained |
|---|---|
| Duration: |
3:09 |
Outcome & Benefits
| Accuracy: |
98% |
|---|---|
| Touchless Rate: |
85% |
| Time Saved: |
From 8m to 1.5m/doc |
| Cost Saved: |
$0.40 per doc |
Functional Details
| Business Tasks: |
Add-on data capture |
|---|---|
| KPI Improved: |
TAT; accuracy |
| Scheduling: |
Real-time / batch |
| Downstream Use: |
Datalake / ERP |
Technical Details
| Model Name/Version: |
GPT-4o-mini |
|---|---|
| Hosting Type: |
API / Cloud |
| Prompt Strategy: |
Schema-guided extraction |
| Guardrails: |
PII masking; validation rules |
| Throughput: |
100 docs/min |
| Latency: |
~2s/doc |
| Data Governance: |
No customer data training |
FAQ
1. What is the Extract Add-on Document and Send It to Any Target workflow?
It is an AI-powered document extraction workflow that uses LLM and vision models to extract structured data from add-on documents and send it to any target system.
2. How does the workflow work?
The workflow ingests add-on documents in PDF or image format, applies LLM and vision models to extract and structure the data, and exports the results in JSON or CSV format to the configured target.
3. What types of documents are supported?
It supports add-on documents such as supporting invoices, attachments, supplementary forms, and related documents provided as PDFs or images.
4. What AI models are used in this workflow?
The workflow uses LLM and vision models provided by Goldfinch AI and OpenAI to accurately extract and structure information from unstructured documents.
5. What is the output of the workflow?
The extracted and structured data is output in JSON or CSV format and can be sent to any downstream system such as a Datalake, ERP, CRM, or analytics platform.
6. Who uses this workflow?
Operations Teams and Finance Teams use this workflow to automate document processing, reduce manual data entry, and improve data accuracy.
7. What are the benefits of automating add-on document extraction?
Automation improves processing speed, reduces errors, standardizes data output, and enables seamless integration with downstream systems for faster decision-making.
Resources
| Blog: |
Amazon Financial Reconciliation: How to Stop Manual Reconciliation-Guide |
|---|---|
| Case Study: |
Add-on Docs Automation |
Case Study
| Industry: |
Logistics / Retail |
|---|---|
| Problem: |
Unstructured add-on docs |
| Solution: |
AI-based document extraction |
| Outcome: |
Faster structured ingestion |
| ROI: |
2-month payback |
