turash/concept/26_summary_implementation_priorities.md

24. Summary & Implementation Priorities

For detailed tactical roadmap with monthly milestones, see 28_project_roadmap.md
For high-level prototype roadmap with phase breakdowns, see 24_prototype_roadmap.md

Lessons Learned Integration

Critical Findings from 30+ Platform Analysis (See Section 30 for full analysis):

1. Critical Mass Requirements: 50+ participants required (not 20-50) for meaningful network effects
2. Local Clustering: ≥70% participants within 5km radius (40% higher match rates)
3. Data Quality Strategy: Progressive refinement with scoring and incentives (prevents "data quality death spiral")
4. Hybrid Automation: 80% automated matches, 20% facilitated (prevents "facilitation dependency")
5. Geographic Expansion: Must achieve 50+ participants AND ≥20% match rate before expanding (prevents "geographic overreach")

Implementation Priority

1. Critical for MVP (Revised based on lessons learned):

   • ADR framework
   • Go 1.25 stack setup (Gin/Fiber, Neo4j driver, NATS/Redis Streams)
   • Basic caching (Redis)
   • Security foundation (JWT, OAuth2)
   • Data Quality Scoring System (prevents data quality death spiral)
   • Economic Calculations Integration (must-have feature for match acceptance)
   • Hybrid Automation Model Definition (automated vs. facilitated routing)
   • 50+ Participants Target (critical mass threshold)

2. Important for Scale:

   • Event-driven architecture with Go workers
   • Graph database optimization
   • Go 1.25 experimental features (JSON v2, GreenTea GC)
   • Monitoring (Prometheus, OpenTelemetry)
   • IoT Integration Priority (30%+ integration rate target by Phase 2)
   • Facilitator Marketplace (enables hybrid automation model)
   • Data Quality Incentives (progressive refinement requirements)

3. Future Enhancements:

   • GraphQL (gqlgen)
   • Mobile app (PWA)
   • Advanced analytics
   • gRPC microservices
   • AI/ML integration (Phase 3+, appropriately timed)

Key Go 1.25 Advantages

• Performance: 10-40% GC overhead reduction with GreenTea
• JSON: 3-10x faster with JSON v2
• Container: Automatic CPU resource management
• Concurrency: Goroutines for efficient parallel processing
• Deployment: Single binary, smaller containers

Technical Spine

That's your technical spine. It's tractable, uses boring reliable tech (Neo4j + Go 1.25 + React), and scales linearly with data.

You can build a credible demo in a quarter and have something real to sell to municipalities or industrial parks before you ever touch pipes or pumps.

Tactical Execution Priorities (From User Feedback Integration)

MVP Focus Areas (address cold start and adoption):

1. Multi-Channel Data Acquisition:
   • Seed Data: Import public registries, building footprints, utility maps, industry association data
   • Forced Adoption: Bundle with mandatory processes (CSRD, energy audits, municipal permits)
   • Partner Ingestion: Utilities/industrial parks provide anonymized facility data
   • Incentive Rewards: Priority matching for complete profiles, fee discounts for data contributors
2. Privacy-First Design: Public/network-only/private tiers with anonymized discovery
3. Low-Capex Matches: Focus on waste pickup, maintenance, shared services (no pipes required)
4. Trust Building: White-label municipal operation, industry association backing
5. Facilitator Ecosystem: Curate and train local engineering/ESG consultancies with platform templates

Revenue Model Evolution:

1. Free Tier: See + Match (drive network effects)
2. Basic: Shared OPEX deals (€50/facility/month)
3. Business: Full symbiosis + facilitators (€150/facility/month)
4. Enterprise: Integration + white-label (€500/facility/month)

Channel Strategy:

• Utility Partnerships: They provide data, you provide UI
• Municipal Dashboards: Charge cities, give companies free access
• Facilitator Marketplace: 10-20% commission on intermediated deals

Success Metrics:

• 20 companies, ≥5 actionable matches, ≥3 implemented connections
• ≥60% data completion rate, clear revenue path within 6 months

Each recommendation should be evaluated against project constraints (time, budget, team expertise) and documented as an ADR when implemented.