docs/PROJECT_PLAN.md

Project Plan: Agentic Business Digitization Framework

Executive Summary

Project Vision

Build an intelligent, agentic system that transforms unstructured business documents into structured digital business profiles automatically, reducing manual digitization time from days to minutes.

Business Problem

Small and medium businesses maintain critical information in chaotic folder structures containing mixed media - PDFs, Word documents, spreadsheets, images, and videos. Converting this into structured digital presence requires:

• Manual data entry (error-prone, time-consuming)
• Technical expertise to structure information
• Significant time investment per business
• Inconsistent data quality

Solution Approach

An AI-powered agentic framework that:

1. Ingests business document folders (via ZIP upload)
2. Automatically extracts and structures information
3. Generates comprehensive business profiles
4. Produces product/service inventories
5. Provides dynamic UI for viewing and editing

Project Scope

In Scope

• Use Case 1 ONLY: Agentic Chat Framework for Business Digitization
• ZIP file ingestion and extraction
• Multi-format document parsing (PDF, DOCX, Excel, images, videos)
• Automated business profile generation
• Product and service inventory creation
• Dynamic UI rendering based on business type
• Post-digitization editing interface
• Vectorless RAG implementation

Out of Scope

• Hotel digitization use case
• Real-time document processing (not batch-oriented)
• Multi-user collaboration features
• Cloud storage integration
• API for third-party integrations
• Mobile app development
• Payment processing integration
• Features not explicitly mentioned in requirements

Success Metrics

Technical Metrics

Metric	Target	Measurement Method
Document parsing accuracy	>90%	Manual validation against sample set
Table extraction accuracy	>85%	Comparison with ground truth
Processing time (10 docs)	<2 minutes	Automated benchmarking
Image extraction success	95%	Validation of embedded images
Schema completeness	70%+ fields populated	Automated scoring
System uptime	99%	Error monitoring

Business Metrics

Metric	Target	Impact
Time saved vs manual	80% reduction	~4 hours → ~45 minutes
Data quality improvement	60% fewer errors	Automated validation
User satisfaction	4+/5 rating	Post-digitization survey

Project Phases

Phase 0: Planning & Documentation (Week 1)

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
• Dependencies identified

Phase 1: ZIP Ingestion & File Discovery (Week 2)

Objectives:

• Implement secure ZIP extraction
• Build file type detection
• Create file hierarchy mapping

Deliverables:

• ZIP extraction module
• File classifier
• Directory structure parser
• Unit tests for file handling

Success Criteria:

• Handles ZIP files up to 500MB
• Correctly identifies all supported file types
• Preserves directory structure
• Error handling for corrupted files

Phase 2: Document Parsing & Text Extraction (Week 3)

Objectives:

• Implement PDF text extraction
• Build DOCX parser
• Create text normalization pipeline

Deliverables:

• PDF parser module (pdfplumber)
• DOCX parser module (python-docx)
• Text cleaning utilities
• Parser factory pattern implementation

Success Criteria:

• Extracts text from PDFs with >90% accuracy
• Handles DOCX with complex formatting
• Preserves document structure context
• Handles multi-language documents

Phase 3: Table Extraction & Structuring (Week 4)

Objectives:

• Detect tables in documents
• Convert tables to structured format
• Handle complex table layouts

Deliverables:

• Table detection algorithms
• Table-to-JSON converter
• Pricing table parser
• Itinerary table parser

Success Criteria:

• Detects tables with >85% accuracy
• Correctly extracts pricing information
• Handles merged cells and complex layouts
• Validates extracted data types

Phase 4: Media Extraction (Week 5)

Objectives:

• Extract embedded images from PDFs
• Process standalone media files
• Generate media metadata

Deliverables:

• Image extraction module (pdf2image)
• Media file handler
• Image metadata generator
• Media-document association logic

Success Criteria:

• Extracts 95%+ embedded images
• Handles JPEG, PNG, GIF formats
• Detects video files
• Maintains image quality

Phase 5: LLM-Assisted Schema Mapping (Week 6-7)

Objectives:

• Design Claude API integration
• Create prompt templates
• Implement schema mapping logic

Deliverables:

• Claude API wrapper
• Prompt engineering library
• Context window management
• Field extraction agents
• Product/service classifier

Success Criteria:

• Correctly classifies business type
• Maps 70%+ fields accurately
• Handles missing information gracefully
• Processes context within token limits

Phase 6: Schema Validation (Week 8)

Objectives:

• Build validation rules engine
• Implement data quality scoring
• Create error recovery mechanisms

Deliverables:

• Pydantic schema validators
• Completeness scoring system
• Data quality metrics
• Validation report generator

Success Criteria:

• Catches invalid data formats
• Scores profile completeness
• Flags suspicious patterns
• Provides actionable feedback

Phase 7: Business Profile Generation (Week 9)

Objectives:

• Assemble final structured output
• Generate JSON profiles
• Implement profile versioning

Deliverables:

• Profile assembly engine
• JSON schema generator
• Export utilities
• Profile versioning system

Success Criteria:

• Generates valid JSON output
• Includes all extracted data
• Maintains data provenance
• Supports multiple output formats

Phase 8: Dynamic UI Rendering (Week 10-11)

Objectives:

• Build React frontend
• Implement conditional rendering
• Create editing interface

Deliverables:

• React component library
• Product inventory display
• Service inventory display
• Editing forms
• Media gallery

Success Criteria:

• Renders profiles dynamically
• Handles product/service/mixed types
• Provides intuitive editing
• Responsive design

Phase 9: Integration & Testing (Week 12)

Objectives:

• End-to-end integration testing
• Performance optimization
• Bug fixes

Deliverables:

• Integration test suite
• Performance benchmarks
• Bug fix documentation
• User acceptance testing

Success Criteria:

• All components work together
• Meets performance targets
• No critical bugs
• Passes UAT

Phase 10: Documentation & Deployment (Week 13)

Objectives:

• Create user documentation
• Deployment guides
• System maintenance docs

Deliverables:

• User manual
• API documentation
• Deployment guide
• Troubleshooting guide

Success Criteria:

• Complete documentation
• Successful deployment
• Team trained on system

Resource Requirements

Technical Resources

• Development Environment: Python 3.10+, Node.js 18+, React
• Cloud Resources: Claude API credits ($500 estimated)
• Storage: Local filesystem (expandable to cloud)
• Compute: 8GB+ RAM, multi-core CPU for parallel processing

Human Resources

Role	Time Commitment	Responsibilities
Senior Engineer	Full-time (13 weeks)	Architecture, core development
Frontend Developer	Part-time (4 weeks)	UI development, editing interface
QA Engineer	Part-time (3 weeks)	Testing, validation
Technical Writer	Part-time (1 week)	Documentation

External Dependencies

• Anthropic Claude API access
• Python libraries: PyPDF2, pdfplumber, python-docx, openpyxl, Pillow
• React ecosystem: react-dropzone, shadcn/ui
• Testing frameworks: pytest, Jest

Risk Management

Technical Risks

Risk	Likelihood	Impact	Mitigation
PDF parsing accuracy issues	High	High	Multiple parser fallbacks, manual review option
Complex table extraction	High	Medium	Rule-based + ML hybrid approach
LLM hallucination	Medium	High	Strict validation, grounding in source docs
Large file processing timeout	Medium	Medium	Chunking, parallel processing
Embedded image quality loss	Low	Medium	Preserve original resolution

Project Risks

Risk	Likelihood	Impact	Mitigation
Scope creep	Medium	High	Strict requirements adherence
API cost overrun	Low	Medium	Token usage monitoring
Timeline delays	Medium	Medium	Weekly progress reviews
Incomplete requirements	Low	High	Early validation with stakeholders

Quality Assurance

Testing Strategy

1. Unit Testing: Each module independently tested
2. Integration Testing: End-to-end workflows validated
3. Performance Testing: Benchmark against targets
4. Acceptance Testing: Real business documents validation

Test Data

• Sample 1: Restaurant (menu PDFs, images)
• Sample 2: Travel agency (package PDFs, itineraries)
• Sample 3: Retail store (product lists, pricing)
• Sample 4: Service business (service descriptions)
• Sample 5: Mixed media (various formats)

Quality Gates

• Code review required for all commits
• 80%+ test coverage
• No critical bugs before phase completion
• Documentation updated with code changes

Communication Plan

Status Updates

• Daily: Team standup (15 min)
• Weekly: Progress report to stakeholders
• Bi-weekly: Sprint review and planning
• Monthly: Executive summary

Documentation

• Technical decisions recorded in ADRs
• Progress tracked in project management tool
• Code documented inline and in wiki
• API documentation auto-generated

Assumptions

1. Business documents are in standard formats (not proprietary)
2. Claude API will maintain stable pricing and availability
3. Average business folder contains 10-50 documents
4. Documents are primarily in English (multi-language support future phase)
5. Users have basic technical literacy for ZIP upload
6. Internet connectivity available for LLM API calls

Constraints

1. Budget: Limited to $2000 for API costs
2. Timeline: 13-week delivery deadline
3. Technology: Python backend, React frontend (per requirements)
4. Scope: Use Case 1 only
5. Data Privacy: No external data transmission except to Claude API

Post-Launch Plan

Maintenance

• Monthly dependency updates
• Quarterly performance reviews
• Bug fix releases as needed

Future Enhancements (Post V1.0)

• Multi-language support
• Cloud storage integration
• Batch processing for multiple businesses
• Advanced analytics on extraction quality
• Template library for common business types
• Export to multiple formats (CSV, XML)

Conclusion

This project plan provides a structured approach to building a production-grade agentic business digitization framework. Success depends on:

• Strict adherence to documented requirements
• Phased implementation with validation gates
• Continuous testing and quality assurance
• Clear communication and documentation

The 13-week timeline is ambitious but achievable with focused execution and proper risk management.