RFE Builder - AI-Powered Workflow Platform (Phased Implementation) #17
Description
RFE Builder - AI-Powered Workflow Platform
Overview
Based on video analysis of Tushar's demo, create an AI-first Streamlit application that transforms RFE Council workflow management with intelligent automation and conversational interfaces.
Video Analysis Key Findings
- AI Chatbot Interface: PM Chatbot - RFE Assistant with contextual guidance
- Smart Suggestions: Intelligent prompts and auto-completion
- Enterprise Integration: Jira/project management tool connectivity
- Structured RFE Documents: Clear categorization with success criteria
🚀 PHASED IMPLEMENTATION PLAN
Phase 1: Foundation & Core Workflow ⏱️ 2-3 weeks
Goal: Basic workflow visualization with agent interactions
Deliverables
- Basic Streamlit multi-page application
- Mermaid diagram integration for workflow visualization
- Agent role pages (Parker, Archie, Stella, Olivia, Lee, Taylor, Derek)
- Simple RFE data model and state management
- Basic step progression and status tracking
File Structure
/demos/rfe-builder/
├── app.py # Main application
├── pages/agent_*.py # Individual agent pages
├── components/workflow.py # Basic workflow display
├── data/rfe_models.py # Data structures
└── requirements.txt # Basic dependencies
Success Criteria
- All 7 workflow steps navigable
- Agent-specific interfaces functional
- RFE state persistence across sessions
- Visual workflow matches mermaid diagram
Phase 2: Conversational Interface ⏱️ 3-4 weeks
Goal: AI-powered chat interface for RFE creation and management
Deliverables
- Chat-first RFE creation interface
- Natural language processing for requirement extraction
- Context-aware form generation
- AI-assisted decision support for each agent
- Smart suggestions and auto-completion
Enhanced Structure
/demos/rfe-builder/
├── components/
│ ├── chat_interface.py # AI chat component
│ ├── ai_assistants.py # Role-specific AI helpers
│ └── nlp_processor.py # Natural language processing
├── ai_models/
│ ├── rfe_classifier.py # RFE type classification
│ └── completeness_check.py # Automated validation
Success Criteria
- Conversational RFE creation working
- Agent-specific AI guidance functional
- Natural language to structured data conversion
- Context-aware recommendations for each role
Phase 3: Enterprise Integration ⏱️ 2-3 weeks
Goal: Real-world workflow tool connectivity
Deliverables
- Jira/GitHub Issues integration
- Bi-directional data synchronization
- Automated ticket creation and updates
- Webhook support for real-time updates
- API gateway for third-party integrations
Integration Structure
/demos/rfe-builder/
├── integrations/
│ ├── jira_connector.py # Enterprise tool integrations
│ ├── github_connector.py # GitHub Issues API
│ ├── webhook_handlers.py # Real-time updates
│ └── api_gateway.py # External API management
Success Criteria
- RFEs sync with external ticketing systems
- Status updates flow bi-directionally
- Automated project setup and assignment
- Real-time collaboration features working
Phase 4: Advanced Intelligence ⏱️ 3-4 weeks
Goal: AI-powered decision support and automation
Deliverables
- Advanced RFE analysis and recommendations
- Automated impact assessment
- Predictive workflow optimization
- Historical data analysis and insights
- Custom reporting and dashboards
Intelligence Structure
/demos/rfe-builder/
├── ai_models/
│ ├── impact_predictor.py # Resource/timeline prediction
│ ├── recommendation_engine.py # Decision support
│ └── analytics_engine.py # Historical analysis
├── dashboards/
│ └── insights_dashboard.py # Analytics and reporting
Success Criteria
- Accurate impact predictions
- Data-driven accept/reject recommendations
- Historical trend analysis
- Custom dashboard creation
📋 TECHNICAL REQUIREMENTS
Dependencies by Phase
Phase 1: streamlit, streamlit-mermaid, pandas, pydantic
Phase 2: + openai, langchain, streamlit-chat
Phase 3: + jira, pygithub, requests, fastapi
Phase 4: + scikit-learn, plotly-dash, sqlalchemy
Acceptance Criteria
- Phase 1: Basic workflow functional with all agent roles
- Phase 2: Conversational interface creates valid RFEs
- Phase 3: External system integration working
- Phase 4: AI recommendations improve decision accuracy
Definition of Done
Each phase must include:
- Working code with tests
- Documentation and setup instructions
- Demo video showing key features
- User acceptance testing completed
Labels
- enhancement
- streamlit
- demo
- rfe-builder
- ai-powered
- phased-implementation