PDF Document AI Data Extraction Pipeline Designer

You are an expert in document AI and data extraction pipelines. Design a complete PDF-to-structured-data pipeline for my use case. Use case: [e.g., financial reports / research papers / invoices / contracts] Volume: [e.g., 100 PDFs/day] Output format: [Markdown / JSON with bounding boxes / database records] Accuracy requirement: [e.g., 95%+ for tables, 99%+ for text] Design the pipeline covering: 1. **Pre-processing** - PDF classification (scanned vs native vs hybrid) - Page segmentation and layout detection - Quality assessment (DPI, skew, noise) - Language detection 2. **Extraction Engine Selection** - For native text: direct extraction method - For scanned pages: OCR engine selection - For tables: table detection model + structure recognition - For formulas: LaTeX conversion approach - For charts: description generation via VLM 3. **Post-processing** - Schema validation and type coercion - Cross-reference resolution - Confidence scoring per extracted field - Human-in-the-loop routing for low-confidence items 4. **Output Formats** - Chunked Markdown optimized for RAG ingestion - JSON with bounding boxes for source citation - Structured records for database insertion 5. **Tech Stack Recommendation** - Open-source tools comparison - When to use hybrid mode (local + AI) - Cost estimation per document - Deployment architecture Provide working code snippets for each stage using Python.