Pharma Document Understanding- Data Extraction Automation

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Workflow Name:

Pharma Document Understanding using AI
Purpose:

Automate extraction and processing of unstructured pharma documents
Benefit:

Accelerates document handling; improves accuracy; reduces manual effort
Who Uses It:

Pharma analysts; QA teams; Regulatory staff; Data engineering teams
System Type:

Document Automation; AI Workflow
On-Premise Supported:

Yes
Supported Protocols:

HTTPS / REST
Industry:

Pharmaceuticals
Outcome:

Faster processing; improved data accessibility
Category: Tags: , , , , , , , , ,

Description

Problem Before:

Manual extraction from SharePoint was slow; error-prone; and inconsistent
Solution Overview:

AI extracts; classifies; and validates key pharma document data and pushes it to the Data Lake
Key Features:

OCR; NLP extraction; field classification; SharePoint connector; Data Lake integration
Business Impact:

70% faster document processing; 90% reduction in errors
Productivity Gain:

3x more documents processed per analyst
Cost Savings:

Reduces operational cost by 40%
Security & Compliance:

Pharma-grade compliance; audit logging

Pharma Document Understanding 

Streamline Pharma Document Understanding by automating the extraction, structuring, and validation of critical data from documents. This no-code workflow ensures faster, more accurate processing of pharma files, reducing manual effort and compliance risks.

Intelligent Data Mapping & Validation

With automated data mapping, the system identifies and extracts key fields such as product details, batch data, specifications, and compliance attributes. It validates, standardizes, and organizes the information for downstream systems, enabling higher accuracy, faster reviews, and improved regulatory readiness.

Watch Demo

Video Title:

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

07:30

Outcome & Benefits

Time Savings:

70% faster document handling
Cost Reduction:

40% cost reduction
Accuracy:

90%+ accuracy
Productivity:

3x more documents per analyst

Industry & Function

Function:

Document Processing; Workflow Automation
System Type:

Document Automation; AI Workflow
Industry:

Pharmaceuticals

Functional Details

Use Case Type:

Pharma Document Automation
Source Object:

Unstructured pharmaceutical documents
Target Object:

Data Lake structured records
Scheduling:

Hourly or on-demand
Primary Users:

Pharma QA analysts; regulatory teams; data consumers
KPI Improved:

Processing time; error reduction; workforce productivity
AI/ML Step:

OCR + NLP document understanding
Scalability Tier:

Enterprise-grade; scalable ingestion

Technical Details

Source Type:

SharePoint
Source Name:

Pharma SharePoint Repository
API Endpoint URL:

/api/v1/pharma-doc-ai
HTTP Method:

POST / GET
Auth Type:

OAuth 2.0
Rate Limit:

1000 requests/min
Pagination:

Page-based
Schema/Objects:

Document metadata; extracted content; annotations
Transformation Ops:

Text extraction; NLP classification; data normalization
Error Handling:

Retry logic; event logging; exception queue
Orchestration Trigger:

Scheduled or event-based
Batch Size:

100 documents per batch
Parallelism:

5 threads
Target Type:

Data Lake
Target Name:

Enterprise Data Lake
Target Method:

POST / PUT
Ack Handling:

Data Lake confirms ingestion
Throughput:

1000 docs/hour
Latency:

<2 minutes per document
Logging/Monitoring:

Cloud logs and monitoring dashboard

Connectivity & Deployment

On-Premise Supported:

Yes
Supported Protocols:

HTTPS / REST
Cloud Support:

AWS; Azure; Private Cloud
Security & Compliance:

Pharma-grade compliance; audit logging

FAQ

1. What is Pharma Document Understanding?

Pharma Document Understanding refers to the automated extraction, classification, and validation of data from pharmaceutical documents to streamline processing and reduce manual tasks.

2. How does automated document data extraction work?

The system uses OCR, AI models, and data mapping to identify key fields, extract structured information, and validate accuracy before sending data to downstream systems.

3. What types of pharma documents can be processed?

It supports SOPs, COAs, batch records, product specs, regulatory documents, quality reports, and other pharma-related files.

4. How is data accuracy ensured during extraction?

AI-driven validation checks, rule-based verification, and automated formatting ensure high accuracy and compliance with pharma documentation standards.

5. Can the workflow integrate with other pharma systems?

Yes. The extracted data can be pushed to LIMS, QMS, ERP systems, or custom pharma applications for faster decision-making and seamless data flow.

6. What are the benefits of automating pharma document extraction?

Automation reduces manual workload, speeds up reviews, improves data accuracy, ensures compliance readiness, and enhances operational efficiency.

Case Study

Customer Name:

Global Pharma Company
Problem:

Slow and manual document extraction from SharePoint
Solution:

AI-based Pharma Document Understanding workflow
ROI:

Faster approvals; redeployment of staff; 3-month payback
Industry:

Pharmaceuticals
Outcome:

Faster processing; improved data accessibility

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