docs/EXECUTION_ROADMAP.md

Execution Roadmap: Agentic Business Digitization Framework

Timeline Overview

Total Duration: 13 weeks Methodology: Agile with weekly sprints Team Size: 1-2 developers (can scale)

Phase Breakdown

Week 1: Foundation & Documentation ✅

Goals: Complete all planning and documentation

Deliverables:

• PROJECT_PLAN.md
• SYSTEM_ARCHITECTURE.md
• AGENT_PIPELINE.md
• DATA_SCHEMA.md
• DOCUMENT_PARSING_STRATEGY.md
• MULTIMODAL_PROCESSING.md
• RAG_STRATEGY.md
• EXECUTION_ROADMAP.md
• CODING_GUIDELINES.md

Success Criteria:

• All documentation reviewed and approved
• Technical approach validated
• Development environment set up

---

Week 2: ZIP Ingestion & File Discovery

Focus: Build foundation for file handling

Tasks

1. Project Setup (Day 1-2)

   # Create project structure
   mkdir -p backend/{agents,parsers,indexing,validation,utils}
   mkdir -p frontend/src/{components,pages,hooks}
   mkdir -p storage/{uploads,extracted,profiles,index}
   mkdir -p tests/{unit,integration}
   
   # Initialize Python project
   poetry init
   poetry add anthropic pydantic python-dotenv
   
   # Initialize React project
   cd frontend && npm create vite@latest . -- --template react-ts
   

2. ZIP Extraction Module (Day 2-3)

   • Implement FileDiscoveryAgent
   • Security checks (path traversal, zip bombs)
   • File type detection
   • Directory structure mapping
   • File: backend/agents/file_discovery.py

3. File Classification (Day 3-4)

   • MIME type detection
   • Extension-based fallback
   • Magic number validation
   • File: backend/utils/file_classifier.py

4. Storage Organization (Day 4-5)

   • Extracted file management
   • Job directory structure
   • Cleanup utilities
   • File: backend/utils/storage_manager.py

5. Testing (Day 5)

   • Unit tests for file discovery
   • Test with sample ZIP files
   • Edge case validation

Deliverables:

• ✅ ZIP extraction working
• ✅ File classification accurate
• ✅ 90%+ test coverage
• ✅ Sample data processed

---

Week 3: Document Parsing & Text Extraction

Focus: Implement multi-format document parsers

Tasks

1. PDF Parser (Day 1-2)

   • Integrate pdfplumber
   • Text extraction with layout preservation
   • Page-level processing
   • PyPDF2 fallback
   • File: backend/parsers/pdf_parser.py

2. DOCX Parser (Day 2-3)

   • python-docx integration
   • Paragraph extraction
   • Style preservation
   • File: backend/parsers/docx_parser.py

3. Parser Factory (Day 3)

   • Factory pattern implementation
   • Parser selection logic
   • Error handling
   • File: backend/parsers/parser_factory.py

4. Text Normalization (Day 4)

   • Whitespace cleaning
   • Unicode handling
   • Artifact removal
   • File: backend/utils/text_utils.py

5. Testing & Validation (Day 5)

   • Parser unit tests
   • Real document testing
   • Performance benchmarks

Deliverables:

• ✅ PDF parsing >90% accuracy
• ✅ DOCX parsing complete
• ✅ Fallback strategies working
• ✅ Performance: <5s for 10-page doc

---

Week 4: Table Extraction & Structuring

Focus: Intelligent table detection and extraction

Tasks

1. Table Detection (Day 1-2)

   • pdfplumber table extraction
   • Visual layout analysis
   • Table validation logic
   • File: backend/agents/table_extraction.py

2. Table Cleaning (Day 2-3)

   • Normalize table data
   • Handle merged cells
   • Remove empty rows/columns
   • File: backend/utils/table_utils.py

3. Table Classification (Day 3-4)

   • Pricing table detection
   • Itinerary table detection
   • Specification table detection
   • Rule-based classification
   • File: backend/agents/table_classifier.py

4. Table to JSON (Day 4)

   • Structured conversion
   • Schema mapping
   • Data validation

5. Integration Testing (Day 5)

   • End-to-end table extraction
   • Various table formats
   • Edge cases

Deliverables:

• ✅ Table extraction >85% accuracy
• ✅ Type classification working
• ✅ JSON conversion complete
• ✅ Handles complex tables

---

Week 5: Media Extraction

Focus: Extract and organize images/videos

Tasks

1. PDF Image Extraction (Day 1-2)

   • Embedded image detection
   • Image data extraction
   • Quality preservation
   • File: backend/agents/media_extraction.py

2. DOCX Image Extraction (Day 2-3)

   • ZIP-based extraction
   • Media file handling
   • Format detection

3. Standalone Media Processing (Day 3-4)

   • Image file handling
   • Video metadata extraction
   • Deduplication logic
   • File: backend/utils/media_utils.py

4. Image Quality Assessment (Day 4)

   • Resolution checking
   • Format validation
   • Quality scoring
   • File: backend/utils/image_quality.py

5. Testing (Day 5)

   • Image extraction tests
   • Deduplication validation
   • Performance optimization

Deliverables:

• ✅ 95%+ image extraction success
• ✅ Deduplication working
• ✅ Quality assessment complete
• ✅ Supports JPEG, PNG, GIF

---

Week 6-7: LLM-Assisted Schema Mapping

Focus: Intelligent field extraction using Claude

Week 6 Tasks

1. Claude API Integration (Day 1-2)

   • API client setup
   • Authentication
   • Rate limiting
   • Token management
   • File: backend/utils/claude_client.py

2. Vision Agent (Day 2-4)

   • Image analysis implementation
   • Prompt engineering
   • Batch processing
   • Error handling
   • File: backend/agents/vision_agent.py

3. Image Association Logic (Day 4-5)

   • Match images to products/services
   • Context-based matching
   • Confidence scoring
   • File: backend/agents/image_associator.py

Week 7 Tasks

4. Business Type Classification (Day 1)

   • Product vs Service vs Mixed
   • LLM-based classification
   • Confidence thresholds
   • File: backend/agents/business_classifier.py

5. Field Extraction Agents (Day 2-4)

   • Business info extraction
   • Product extraction
   • Service extraction
   • Prompt templates
   • Files:
     • backend/agents/schema_mapping.py
     • backend/prompts/field_extraction.py

6. Integration & Testing (Day 4-5)

   • End-to-end LLM pipeline
   • Token usage monitoring
   • Accuracy validation

Deliverables:

• ✅ Claude integration complete
• ✅ Vision analysis working
• ✅ Field extraction >70% accuracy
• ✅ Token usage within budget

---

Week 8: Indexing & RAG Implementation

Focus: Build vectorless page index

Tasks

1. Keyword Extraction (Day 1-2)

   • Tokenization
   • Stopword removal
   • N-gram generation
   • Entity extraction
   • File: backend/indexing/keyword_extractor.py

2. Index Builder (Day 2-3)

   • Inverted index creation
   • Page reference storage
   • Table indexing
   • Media indexing
   • File: backend/indexing/index_builder.py

3. Query Processor (Day 3-4)

   • Query normalization
   • Synonym expansion
   • Term weighting
   • File: backend/indexing/query_processor.py

4. Context Retriever (Day 4-5)

   • Page ranking
   • Context building
   • Relevance scoring
   • File: backend/indexing/retriever.py

5. Testing (Day 5)

   • Retrieval accuracy tests
   • Performance benchmarks
   • Edge case validation

Deliverables:

• ✅ Index building complete
• ✅ Retrieval working
• ✅ Fast query response (<100ms)
• ✅ Accurate context extraction

---

Week 9: Schema Validation & Profile Generation

Focus: Validate and assemble final profiles

Tasks

1. Pydantic Validators (Day 1-2)

   • Schema validation rules
   • Type checking
   • Format validation
   • File: backend/validation/schema_validator.py

2. Completeness Scoring (Day 2-3)

   • Field population metrics
   • Category scoring
   • Overall completeness
   • File: backend/validation/completeness.py

3. Data Quality Checks (Day 3-4)

   • Cross-field validation
   • Business rule enforcement
   • Anomaly detection
   • File: backend/validation/quality_checker.py

4. Profile Assembly (Day 4)

   • Combine all extracted data
   • Apply validation
   • Generate metadata
   • File: backend/agents/profile_assembler.py

5. Export Utilities (Day 5)

   • JSON export
   • Schema-compliant output
   • Version tracking
   • File: backend/utils/export_utils.py

Deliverables:

• ✅ Validation rules complete
• ✅ Quality scoring working
• ✅ Profile generation successful
• ✅ No invalid outputs

---

Week 10-11: Frontend Development

Focus: Build dynamic UI

Week 10 Tasks

1. Project Setup (Day 1)

   • React + TypeScript
   • Tailwind CSS
   • shadcn/ui components
   • State management (Zustand)

2. Upload Component (Day 1-2)

   • react-dropzone integration
   • Progress tracking
   • Validation feedback
   • File: frontend/src/components/UploadZone.tsx

3. Profile Viewer (Day 2-4)

   • Business info display
   • Conditional rendering
   • Media gallery
   • Files:
     • frontend/src/components/ProfileViewer.tsx
     • frontend/src/components/BusinessInfo.tsx

4. Product Display (Day 4-5)

   • Product card component
   • Grid layout
   • Detail modal
   • File: frontend/src/components/ProductInventory.tsx

Week 11 Tasks

5. Service Display (Day 1-2)

   • Service card component
   • Itinerary display
   • FAQ accordion
   • File: frontend/src/components/ServiceInventory.tsx

6. Edit Interface (Day 2-4)

   • React Hook Form integration
   • Field editing
   • Media upload/remove
   • Save/discard
   • File: frontend/src/components/EditProfile.tsx

7. Styling & Polish (Day 4-5)

   • Responsive design
   • Loading states
   • Error handling
   • Animations

Deliverables:

• ✅ Full UI working
• ✅ Dynamic rendering
• ✅ Editing functional
• ✅ Responsive design

---

Week 12: Integration & Testing

Focus: End-to-end testing and optimization

Tasks

1. Backend-Frontend Integration (Day 1-2)

   • API endpoints
   • Request/response handling
   • Error propagation

2. End-to-End Testing (Day 2-3)

   • Complete workflow tests
   • Real business documents
   • Multiple business types

3. Performance Optimization (Day 3-4)

   • Parallel processing
   • Caching
   • Database queries
   • Memory management

4. Bug Fixes (Day 4-5)

   • Issue tracking
   • Priority fixes
   • Regression testing

5. User Acceptance Testing (Day 5)

   • Stakeholder demo
   • Feedback collection
   • Final adjustments

Deliverables:

• ✅ All components integrated
• ✅ No critical bugs
• ✅ Performance targets met
• ✅ UAT passed

---

Week 13: Documentation & Deployment

Focus: Final documentation and deployment

Tasks

1. User Documentation (Day 1-2)

   • User manual
   • How-to guides
   • FAQ
   • File: docs/USER_MANUAL.md

2. API Documentation (Day 2)

   • Endpoint documentation
   • Request/response examples
   • Error codes
   • File: docs/API.md

3. Deployment Setup (Day 3-4)

   • Docker containerization
   • Environment configuration
   • Deployment scripts
   • Files:
     • Dockerfile
     • docker-compose.yml
     • deploy.sh

4. Monitoring Setup (Day 4)

   • Logging configuration
   • Error tracking
   • Performance monitoring

5. Launch (Day 5)

   • Production deployment
   • Smoke testing
   • Handoff to ops

Deliverables:

• ✅ Complete documentation
• ✅ Deployed to production
• ✅ Monitoring active
• ✅ Team trained

---

Risk Mitigation Plan

High Priority Risks

Risk	Mitigation	Contingency
PDF parsing accuracy	Test with diverse samples early	Have manual review fallback
LLM token costs exceed budget	Monitor usage daily, optimize prompts	Reduce image batch size
Complex table extraction fails	Implement multiple strategies	Mark for manual review
Timeline delays	Weekly progress reviews, buffer time	Reduce scope if needed

Monitoring Checkpoints

Weekly Status Review:

• Completed tasks vs planned
• Blockers and risks
• Budget status (LLM tokens)
• Quality metrics

Go/No-Go Decision Points:

• Week 3: Document parsing accuracy >85%
• Week 7: LLM extraction accuracy >65%
• Week 10: UI functionality complete
• Week 12: UAT approval

---

Success Metrics

Technical Metrics

Metric	Target	Measurement
Document parsing accuracy	>90%	Manual validation on 50 samples
Table extraction accuracy	>85%	Comparison with ground truth
Processing time (10 docs)	<2 minutes	Automated benchmarking
Image extraction success	>95%	Embedded image count validation
Schema completeness	>70% fields	Automated field population check
LLM token usage	<50k per job	API usage tracking

Business Metrics

Metric	Target	Impact
Time saved vs manual	>80%	User surveys
User satisfaction	>4/5	Post-launch survey
Error rate reduction	>60%	Validation comparison

---

Post-Launch Roadmap

Month 1-3: Stabilization

• Monitor production usage
• Fix bugs reported by users
• Optimize performance based on real usage
• Collect user feedback

Month 4-6: Enhancements

• Multi-language support
• Additional file formats
• Advanced analytics
• Batch processing

Month 7-12: Scale

• Cloud storage integration
• API for third-party integrations
• Mobile app
• Enterprise features

---

Conclusion

This roadmap provides a clear path from inception to production deployment. The phased approach allows for:

• Incremental validation at each stage
• Risk mitigation through early testing
• Flexibility to adjust based on learnings
• Quality assurance built into process

Success depends on disciplined execution, continuous testing, and willingness to iterate based on feedback.