Automating Academic Publishing Workflows for EAI Publishing House
Client Industry: Academic Publications
Tech Stack
- Core Platform: Python 3.9
- Database: PostgreSQL 14
- Processing: PyMuPDF, spaCy
- Infrastructure: AWS Lambda, S3
- Compliance: JATS XML Schema 1.2
Our Challenge
Automating Academic Publishing Workflows
- Manual Processes: Staff spent 45 minutes per paper on data entry
- Metadata Inconsistencies: 12% error rate in author affiliations and DOIs
- Publication Delays: 4-week backlog in proceedings publication
- Compliance Risks: Frequent formatting issues with CrossRef/PubMed submissions
Proposed Solution
As an IT consulting and support provider, we implemented a phased approach:
System Architecture
- Automated Ingestion Layer
- OJS API integration for paper retrieval
- PostgreSQL database for metadata management
- Processing Engine
- PDF-to-JSON conversion via NLP API
- AI-based content classification (text, figures, tables)
- Automated validation checks
- Output Generation
- JATS-standard XML for compliance
- Bulk export to publishing platforms
- Automated error reporting
Key Features
Feature | Benefit |
Batch processing (500+ papers/night) | Enabled conference-scale automation |
Dynamic error handling | Reduced manual intervention by 70% |
Cloud-native deployment | 40% cost savings vs on-premise |
Real-time dashboards | Improved workflow visibility |
Business Impact Result
Results & Benefits
Quantitative Benefits:
- 80% faster processing (45 mins → 5 mins per paper)
- 99.5% metadata accuracy (from 88%)
- 100% compliance with PubMed/CrossRef standards
- 40% reduction in cloud infrastructure costs
Qualitative Benefits:
- Improved researcher satisfaction scores
- Enhanced reputation for publication timeliness
- Stronger position in open-access publishing
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