🚀 Apollo Project Roadmap

The Challenge

ApolloDKubeCNET currently has 6-7 staff spending 1 hour/day manually processing ~1,000 purchase orders per month, leading to 840 hours/month of manual work, batch delays, and project overruns.

Agent Collaboration Example

🎯 Intelligent Fuzzy Matching with Confidence Evolution

The Problem: Vendors use different terminology for the same parts ("brown pure air tubing" vs "nylon 12 pure air tubing")

Visual Confidence Progression

ML-powered matching with visible learning journey: 68% → 85% → 95% → AUTO

Learning Impact Visualization

Gamification & Progress Tracking

Users earn XP and achievements for training the system:

• 🏆 Level 7: Expert Trainer - 1,247 / 1,500 XP
• ✅ First Match - Confirmed your first fuzzy match
• ✅ Quick Learner - 10 confirmations in one session
• ✅ Pattern Master - Created 5 permanent rules
• ✅ Batch Pro - Applied batch learning 3 times
• 🔒 Automation Hero - Achieve 95% auto-match rate (7 more to unlock!)

💬 NLP-to-SQL Chat Interface - "Ask Apollo"

Natural language queries powered by Claude 3.5 Sonnet

How It Works:

SELECT * FROM purchase_orders 
WHERE vendor_name = 'Acme Corp' 
AND po_date >= '2024-01-01' 
AND po_date < '2024-02-01'

Example Queries:

• "Show me all POs from Acme Corp this month"
• "What's the total spend with Beta Industries?"
• "Which parts have the most qty mismatches?"
• "Show processing accuracy by vendor"
• "Compare Tesseract vs LLM accuracy"
• "What's the average processing time this week?"
• "List all flagged POs from last month"

Security Features:

• ✅ SQL injection prevention
• ✅ Read-only queries (SELECT only)
• ✅ Whitelisted tables
• ✅ Row limits enforced (max 1000)
• ✅ Rate limiting
• ✅ Audit logging

📊 Knowledge Graph Visualization

Interactive graph showing relationships, patterns, and learning over time

What It Shows:

• Part Correlations: "When Slip Coupling is ordered, Elbow 90 is also ordered 78% of the time"
• Vendor Patterns: "Acme Corp typically orders 5-8 line items per PO, avg value $1,200"
• Naming Variations: "brown pure air tubing" = "nylon 12 pure air tubing" (confirmed 3x)
• Confidence Improvements: Part matching 68% → 87% over last 30 days
• Synonym Database: 247 part mappings, 89 vendor variations, 156 description synonyms

Interactive Features:

• Force-directed layout with zoom/pan
• Click nodes for detailed information
• Filter by vendors, parts, POs, correlations
• Time-based evolution view
• Export graph data

📦 Dual Workflow Processing

1. Purchase Order Confirmations (Pre-Receiving)

• Validate prices and quantities
• Flag price changes for approval
• Update expected delivery dates
• Auto-attach to Gemini PO Manager
• Agent: Wendy (PO Agent)

2. Packing Slips (Post-Receiving)

• Match received vs ordered quantities
• Do NOT update prices (already confirmed)
• Update inventory levels
• Close PO line items when complete
• Flag over/under shipments
• Agent: Bond (Slip Agent)

⚡ Real-time Dashboard Features

• Live Updates: WebSocket connections for <1s latency
• Agent Team Chat: Modern Slack/Teams-style interface
• Interactive Analytics: Recharts + D3.js visualizations
• Knowledge Graph Explorer: React Flow with zoom/pan
• Mobile Responsive: Works on all devices
• Dark Theme: Professional indigo color scheme
• 99.9% Uptime: High availability with monitoring

Phase 1: Foundation (Weeks 1-2)

Goal: Establish core infrastructure and development environment

Deliverables:

• Docker environment setup (Docker Compose configuration)
• PostgreSQL 15+ deployment with initial schema
• Redis 7+ for caching and task queuing
• FastAPI application skeleton with project structure
• Basic OCR service with Tesseract integration
• Database schema creation (purchase_orders, line_items, vendors, parts)
• N8N installation and basic configuration
• Development environment documentation

Team:

1 Backend Developer + 1 DevOps Engineer

Milestones:

• ✓ Week 1: Infrastructure setup complete
• ✓ Week 2: Basic OCR processing working

Phase 2: Core OCR & Agent Framework (Weeks 3-4)

Goal: Build intelligent automation core with AI agents

Deliverables:

• OCR LLM integration (GPT-4 Vision / Claude 3.5 Sonnet)
• Confidence scoring system (75% threshold logic)
• Wendy (PO Agent) - Full implementation with PO processing
• Bond (Slip Agent) - Packing slip processing logic
• Gordon (DB Agent) - Database sync and part lookups
• Miley (Email Agent) - IMAP monitoring and notifications
• Agent orchestrator with task assignment
• Inter-agent communication protocol
• Basic error handling and logging

Team:

2 Backend Developers + 1 ML Engineer

Milestones:

• ✓ Week 3: Dual OCR engines operational
• ✓ Week 4: All 4 agents functional

Phase 3: N8N Workflow Integration (Weeks 5-6)

Goal: Connect external systems and automate document ingestion

Deliverables:

• Email monitoring workflow (IMAP every 5 min)
• SharePoint/Drive sync workflow (real-time)
• Telegram bot workflow (instant webhooks)
• Scheduled tasks (cron-based database sync)
• Webhook integrations with FastAPI
• Gemini SQL synchronization workflow
• Document routing logic (PO vs Slip detection)
• Error notification workflows
• Workflow monitoring dashboard

Team:

1 Backend Developer + 1 Integration Specialist

Milestones:

• ✓ Week 5: Email and SharePoint workflows live
• ✓ Week 6: All input channels operational

Phase 4: Dashboard Frontend (Weeks 7-8)

Goal: Build modern, responsive user interface

Deliverables:

• Next.js 14 setup with App Router
• Hero section with Apollo Mechanical Corporation branding
• NLP-to-SQL chat interface (Ask Apollo)
• Agent team chat UI (Slack/Teams style)
• PO processing tabs (Incoming, Processing, Completed, Review)
• Packing slip tabs (Incoming, Processing, Completed, Discrepancies)
• Knowledge graph visualization (React Flow)
• Analytics dashboards (Recharts + D3.js)
• Real-time updates (WebSocket integration)
• Mobile-responsive design
• Dark theme with Apollo branding

Team:

2 Frontend Developers + 1 UI/UX Designer

Milestones:

• ✓ Week 7: Core dashboard components complete
• ✓ Week 8: Full dashboard with real-time features

Phase 5: ML & Learning Systems (Weeks 9-10)

Goal: Implement intelligent learning and normalization

Deliverables:

• Fuzzy matching algorithm (Levenshtein + context)
• Normalization engine with synonym database
• Confidence evolution system (68% → 85% → 95% → AUTO)
• Synonym learning from user selections
• Batch application logic (apply to similar items)
• User training stats and progress tracking
• Gamification system (XP, levels, achievements)
• Vendor-specific pattern recognition
• ML model retraining pipeline
• Learning analytics and insights

Team:

1 ML Engineer + 1 Backend Developer

Milestones:

• ✓ Week 9: Fuzzy matching operational
• ✓ Week 10: Full learning loop implemented

Phase 6: Testing, Training & Launch (Weeks 11-12)

Goal: Ensure production readiness and successful deployment

Deliverables:

• End-to-end testing (all workflows)
• User acceptance testing (UAT) with 6-7 staff
• Performance optimization (sub-2s processing)
• Security audit (penetration testing, code review)
• Comprehensive documentation (user guides, API docs)
• Training materials (videos, tutorials, FAQs)
• Production deployment to wnbpc.de
• User training sessions (3 days, all staff)
• Pilot group testing (2 days)
• Go-live support and monitoring
• Post-launch bug fixes

Team:

Full Team (6 developers) + 1 QA Engineer + 1 Technical Writer

Milestones:

• ✓ Week 11: Testing complete, production ready
• ✓ Week 12: Training complete, system live!

📅 Timeline Summary

💰 Development Budget Breakdown

💸 Annual Infrastructure Costs

⏱️ Time Savings Analysis

📈 3-Year ROI Projection

Payback Period: 12 months (Year 1 + 1 month of Year 2)

Break-even: Month 13

🎯 Success Metrics & KPIs

Processing Metrics

• Volume Capacity: 1,000+ POs/month with room to scale to 5,000+
• Accuracy Target: >90% on top 50-1000 parts (80% of volume)
• Processing Speed: <2 seconds average per document
• Auto-match Rate: 80%+ after 3 months of training
• OCR Accuracy: 95%+ combined (Tesseract + LLM fallback)

Learning Metrics

• Confidence Evolution: 68% → 85% → 95% → AUTO (3 confirmations)
• Synonym Database Growth: 200+ synonyms in first 3 months
• User Confirmations: 50+ per week initially, declining to <10/week
• Manual Review Reduction: 33% reduction every 3 months

Business Metrics

• Time Savings: 4.2 hours saved per week per user
• Cost Avoidance: $150K/year in labor + overtime elimination
• Error Reduction: 50% fewer manual entry errors
• Processing Speed: Real-time vs 2-week batch delays
• Project Overruns: 75% reduction in expedite fees

System Performance

• Uptime SLA: 99.9% (8.76 hours downtime/year max)
• Response Time: <1s for dashboard, <2s for OCR processing
• Concurrent Users: Support 10+ simultaneous users
• Data Retention: 7 years (compliance requirement)

User Satisfaction

• User Rating: Target >4.5/5 from staff
• Training Completion: 100% of 6-7 staff trained
• Daily Active Users: 6-7 staff using system daily
• Feature Adoption: >80% using agent chat, NLP queries

💡 Additional Benefits (Non-Financial)

• Knowledge Preservation: System captures expert knowledge before retirement
• Scalability: Can handle 5x volume increase without additional staff
• Consistency: Standardized processing eliminates human variability
• Audit Trail: Complete history of all processing decisions
• Vendor Insights: Data-driven vendor performance analysis
• Predictive Analytics: Forecast ordering patterns and costs
• Employee Satisfaction: Eliminate tedious manual data entry
• Competitive Advantage: Faster response times to customers