project-pdf-platform-software-design
title: 'Software Design Document: AI-Powered PDF Analysis Platform' type: reference component_type: reference version: 1.0.0 created: '2025-12-27' updated: '2025-12-27' status: archived tags:
- ai-ml
- authentication
- deployment
- security
- testing
- api
- architecture
- automation summary: 'Software Design Document: AI-Powered PDF Analysis Platform Version: 1.0 Date: 2025-10-31 Status: APPROVED Authors: Architecture Team --- Executive Summary 1.1 Purpose Cloud-native platform for intelligent PDF processing with AI-powered content...' moe_confidence: 0.950 moe_classified: 2025-12-31
Software Design Document: AI-Powered PDF Analysis Platform
Version: 1.0
Date: 2025-10-31
Status: APPROVED
Authors: Architecture Team
1. Executive Summary
1.1 Purpose
Cloud-native platform for intelligent PDF processing with AI-powered content extraction, analysis, and cross-validation. Deployed on Google Kubernetes Engine (GKE) with real-time WebSocket communication.
1.2 Scope
- In Scope: PDF upload/management, real-time processing, AI analysis, component extraction, multi-tenant support
- Out of Scope: OCR for scanned images, video processing, third-party integrations (Phase 1)
1.3 Business Goals
- Productivity: 80% reduction in manual PDF analysis time
- Accuracy: 95%+ content extraction accuracy via AI validation
- Scale: Support 10,000+ concurrent users
- Reliability: 99.9% uptime SLA
2. System Architecture Overview
2.1 High-Level Architecture
2.2 Container Architecture
3. Component Design
3.1 Frontend Components
3.2 Backend Services
4. Data Architecture
4.1 Data Models
4.2 Storage Strategy
| Data Type | Storage | Retention | Backup |
|---|---|---|---|
| PDF Files | GCS Standard | 90 days | Daily snapshots |
| Extracted Text | PostgreSQL | 90 days | PITR enabled |
| Analysis Results | PostgreSQL + GCS | 90 days | PITR + snapshots |
| User Sessions | Redis | 24 hours | No backup |
| Audit Logs | Cloud Logging | 365 days | Archived to GCS |
| Metrics | Cloud Monitoring | 30 days | Aggregated to BigQuery |
5. AI Integration Design
5.1 LLM Workflow
5.2 Prompt Engineering Strategy
Hierarchical Prompt Chain:
-
Document Structure Analysis
- Identify sections, headings, metadata
- Extract document type and purpose
- Map content hierarchy
-
Component Extraction
- Extract text blocks with context
- Identify tables and their relationships
- Locate figures and references
-
Cross-Validation
- Verify extracted data consistency
- Validate table calculations
- Check reference integrity
-
Synthesis
- Generate document summary
- Create component relationship graph
- Produce structured JSON output
6. Deployment Architecture
6.1 GKE Cluster Design
6.2 Resource Allocation
| Service | CPU Request | CPU Limit | Memory Request | Memory Limit | Replicas |
|---|---|---|---|---|---|
| API Gateway | 500m | 2000m | 512Mi | 2Gi | 3 |
| PDF Processor | 1000m | 4000m | 1Gi | 4Gi | 2 |
| AI Service | 500m | 2000m | 512Mi | 2Gi | 2 |
| Frontend (CDN) | N/A | N/A | N/A | N/A | N/A |
7. Security Design
7.1 Security Architecture
7.2 Security Controls
| Control | Implementation | Purpose |
|---|---|---|
| Authentication | OAuth 2.0 + JWT | User identity verification |
| Authorization | RBAC + OPA | Resource access control |
| Encryption (Transit) | TLS 1.3 | Data in motion protection |
| Encryption (Rest) | AES-256 via KMS | Data at rest protection |
| Secrets Management | GCP Secret Manager | Credential storage |
| Network Isolation | Private GKE + VPC | Service segmentation |
| API Security | Rate limiting + WAF | DDoS and abuse prevention |
| Data Privacy | PII detection + masking | Compliance (GDPR) |
8. Scalability Design
8.1 Scaling Strategy
8.2 Scaling Thresholds
| Metric | Scale Up Trigger | Scale Down Trigger | Max Replicas |
|---|---|---|---|
| API CPU | >70% for 3min | <30% for 5min | 10 |
| API Memory | >80% for 3min | <40% for 5min | 10 |
| Processor CPU | >80% for 5min | <20% for 10min | 20 |
| Request Queue | >100 pending | <10 pending | N/A |
| Database Connections | >80% pool | <40% pool | N/A |
9. Monitoring & Observability
9.1 Observability Stack
9.2 Key Metrics
Golden Signals:
- Latency: P50, P95, P99 response times
- Traffic: Requests per second
- Errors: Error rate (4xx, 5xx)
- Saturation: CPU, memory, disk usage
Business Metrics:
- PDF processing success rate
- Average processing time per PDF
- AI analysis accuracy (cross-validated)
- User upload volume
- Storage consumption
10. Disaster Recovery
10.1 Backup Strategy
| Component | RPO | RTO | Backup Method |
|---|---|---|---|
| PostgreSQL | 5 min | 1 hour | Point-in-time recovery |
| GCS Files | 0 min | 30 min | Multi-region replication |
| Redis Cache | N/A | 5 min | Rebuild from source |
| Configuration | 1 hour | 15 min | Git + ConfigMap |
| Secrets | 1 hour | 15 min | Secret Manager replication |
10.2 Failure Scenarios
11. Development Workflow
11.1 GitOps Flow
11.2 CI/CD Pipeline
12. Performance Requirements
| Metric | Target | Measurement |
|---|---|---|
| Page Load Time | <2s | Lighthouse |
| API Response Time (P95) | <500ms | Prometheus |
| PDF Upload (10MB) | <5s | End-to-end test |
| PDF Processing (10 pages) | <30s | Processing pipeline |
| AI Analysis | <45s | AI service metrics |
| WebSocket Latency | <100ms | RTT measurement |
| Concurrent Users | 10,000 | Load test |
| Throughput | 1,000 PDFs/hour | Batch processing |
13. Technology Stack
13.1 Frontend
- Framework: React 18 with TypeScript
- State Management: Zustand
- UI Library: Material-UI (MUI)
- Build Tool: Vite
- Testing: Vitest + React Testing Library
13.2 Backend
- API Framework: FastAPI (Python 3.11)
- WebSocket: FastAPI WebSocket + Socket.IO
- PDF Processing: pdfplumber
- AI Integration: Anthropic Python SDK
- Workflow Engine: Temporal
- Testing: pytest + pytest-asyncio
13.3 Infrastructure
- Container Orchestration: Google Kubernetes Engine (GKE)
- Service Mesh: Istio
- Database: Cloud SQL (PostgreSQL 15)
- Cache: Memorystore (Redis 7)
- Storage: Cloud Storage (GCS)
- Monitoring: Cloud Monitoring + Prometheus
- Logging: Cloud Logging + Loki
- Tracing: Cloud Trace + Jaeger
14. Success Metrics
| Metric | Target | Current | Status |
|---|---|---|---|
| System Uptime | 99.9% | TBD | 🟡 Pending |
| Processing Success Rate | 98% | TBD | 🟡 Pending |
| User Satisfaction (NPS) | >50 | TBD | 🟡 Pending |
| AI Accuracy | 95% | TBD | 🟡 Pending |
| Cost per PDF | <$0.10 | TBD | 🟡 Pending |
| Time to Process (avg) | <45s | TBD | 🟡 Pending |
15. Roadmap
Phase 1 (Q1 2025)
- ✅ Core PDF upload and storage
- ✅ Basic text extraction
- ✅ WebSocket real-time updates
- ✅ GKE deployment
Phase 2 (Q2 2025)
- 🟡 AI-powered analysis
- 🟡 Component extraction
- 🟡 Advanced table processing
- 🟡 Multi-language support
Phase 3 (Q3 2025)
- 🔴 OCR integration
- 🔴 Batch processing
- 🔴 API for third-party integration
- 🔴 Advanced analytics dashboard
Appendix A: Glossary
| Term | Definition |
|---|---|
| Component | Extracted sub-element from PDF (text block, table, image) |
| Cross-validation | AI-powered verification of extracted content accuracy |
| Processing Job | Asynchronous task for PDF analysis |
| GKE | Google Kubernetes Engine |
| PITR | Point-in-time Recovery |
| SLA | Service Level Agreement |