turash/docs/concept/28_project_roadmap.md

28. Detailed Project Roadmap & Milestones

For strategic prototype roadmap with high-level phases, see 24_prototype_roadmap.md

Executive Summary

18-month roadmap from concept to market validation with €2.5M seed funding. Focus on de-risking core assumptions while building scalable platform based on Go 1.25 stack, graph-based matching engine, and progressive value delivery through resource matching + service marketplace.

Financial Projections (Revised): Initial projections were overly optimistic. Revised targets align with industry benchmarks for seed-stage B2B SaaS:

• Month 6: €8k-€12k MRR (vs. optimistic €25k)
• Month 12: €25k-€40k MRR (vs. optimistic €150k)
• Month 18: €50k-€80k MRR (€600k-€960k ARR) for Series A readiness
• Conversion Rate: 5-8% free-to-paid (industry average: 2-5%, exceptional: 10-15%)
• Target: Series A readiness (€3M+ ARR typically required) vs. IPO-readiness in original projections

Phase 1: Foundation & MVP (Months 1-3) - €400k Budget

Goal: Validate core assumptions, build heat-matching MVP with manual entry

Month 1: Core Setup & Technical Foundation

Deliverables:

• Team assembly (8 engineers: 4 backend, 2 frontend, 1 DevOps, 1 data; 2 domain experts, 1 BD)
• Development environment setup (Docker Compose, local Neo4j/PostgreSQL)
• Infrastructure provisioning (AWS EKS/GCP GKE, managed Neo4j/PostgreSQL)
• Legal entity formation and seed documents
• ADR Framework: Architecture Decision Records setup and initial decisions
• Go 1.25 Stack Setup: Gin/Fiber selection, Neo4j driver, PostgreSQL/PostGIS with pgx
• Open Standards Foundation: NGSI-LD API integration (2-3 weeks) for smart city interoperability
• Message Queue: NATS or Redis Streams selection (not Kafka for MVP)
• Security Foundation: JWT, OAuth2, RBAC implementation
• Basic CI/CD pipeline (GitHub Actions)

Technical Decisions (ADRs):

• Graph database: Neo4j (migration path to TigerGraph if >10B nodes)
• HTTP framework: Gin (consider Fiber if low latency critical)
• Message queue (MVP): NATS or Redis Streams
• Go 1.25 experimental features: Build with feature flags, fallback to Go 1.23 if not production-ready

Success Metrics:

• Team fully onboarded and productive
• All core infrastructure deployed
• Basic CI/CD pipeline operational
• Development environment documented and replicable

Risks: Team hiring delays, infrastructure complexity, Go 1.25 experimental feature availability Mitigation: Pre-hire key technical roles, use managed services, feature flags for experimental features

Month 2: Data Architecture & Matching Engine Core

Deliverables:

• Graph Database Setup: Neo4j cluster with APOC library
• Spatial Database Setup: PostgreSQL + PostGIS for geospatial queries
• Hybrid Architecture: Neo4j (relationships) + PostGIS (spatial queries) synchronization
• Data Ingestion Pipelines: Manual entry API, CSV upload, basic validation
• Seed Data Collection: Berlin industrial park data (public registries, building footprints)
• Matching Engine Prototype:
  • Spatial pre-filtering (PostGIS 5km radius)
  • Quality matching (temperature compatibility)
  • Temporal overlap calculation
  • Economic scoring (basic cost-benefit)
• Resource Plugin Architecture: Heat exchange plugin (MVP resource type)
• Caching Layer: Redis for match results (15-minute TTL)

Success Metrics:

• 50 businesses with resource flow data (seed data + manual entry)
• Basic matching engine finds 10+ viable heat matches
• Data ingestion reliability >95%
• Matching query latency <2s (p95)

Technical Milestones:

• Graph database schemas deployed (Business, Site, ResourceFlow nodes)
• Spatial indexes created and tested
• Basic REST API endpoints functional
• Seed data quality validation completed

Month 3: MVP Core Features & Pilot Launch

Deliverables:

• Heat Flow Matching: Manual entry only, heat resource type focus
• Map Visualization: React + Mapbox GL, resource flows as colored dots, match connections
• Business Registration: Simple onboarding flow (15 minutes to complete)
• Match Notification System: Basic email notifications (WebSocket in Phase 2)
• Service Marketplace Foundation: Basic structure for future expansion
• Privacy-First Design: Public/network-only/private data tiers
• Free Tier: See + Match functionality (drive network effects)

Success Metrics:

• 20 businesses registered in pilot (Berlin industrial + hospitality)
• 15 heat matches identified and contacted
• 3 expressions of interest for implementation
• ≥60% data completion rate
• ≥5 actionable matches per business

User Testing:

• Pilot user feedback sessions (10-15 businesses)
• UI/UX validation with target users
• Feature prioritization based on user input
• Cold start problem validation

Pilot Selection:

• Vertical focus: "Heat reuse in Berlin industrial + hospitality sector"
• "Cheap-to-act" resources focus: Low-capex matches (shared services, waste pickup)
• Manual data seeding from public sources

Phase 2: MVP Expansion & Revenue (Months 4-6) - €500k Budget

Goal: Expand to multi-resource, automated ingestion, service marketplace, initial revenue

Month 4: Multi-Resource Support & Service Marketplace

Deliverables:

• Water Resource Plugin: Wastewater reuse, water quality matching
• Waste Resource Plugin: Material exchange, by-product reuse
• Economic Calculation Engine:
  • NPV, IRR, payback period calculations
  • Sensitivity analysis
  • Scenario modeling
• Enhanced Matching Algorithms:
  • Multi-criteria scoring (quality, temporal, economic, distance, trust)
  • Ranking engine with diversity consideration
  • Fallback matching (relaxed constraints)
• Service Marketplace MVP:
  • Maintenance services matching
  • Shared service opportunities
  • Group buying foundation
• Privacy-Preserving Matching: Anonymized discovery, network-only visibility

Success Metrics:

• 3 resource types fully supported (heat, water, waste)
• Economic calculations accurate to ±10%
• 50% increase in match quality
• Service marketplace: 5-10 service providers registered

Technical Milestones:

• Resource plugin architecture proven (3 plugins working)
• Economic calculator validated against manual calculations
• Matching algorithm performance maintained (<2s p95 latency)

Month 5: Automated Data Ingestion & Event-Driven Architecture

Deliverables:

• Event-Driven Architecture:
  • NATS/Redis Streams for event processing
  • Event handlers for ResourceFlow changes
  • Incremental matching (only affected subgraphs)
• ERP/SCADA API Integrations:
  • SAP, Oracle basic integration (REST API)
  • OPC UA protocol support
• IoT Device Connectivity:
  • Modbus RTU/TCP support
  • MQTT broker integration
  • OGC SensorThings API (Phase 2 priority from prototype roadmap)
• Data Quality Validation Pipeline:
  • Precision levels (rough/estimated/measured)
  • Device-signed validation
  • Data quality scoring
• Background Processing: Go workers with channel-based processing

Success Metrics:

• 80% reduction in manual data entry (for early adopters with integrations)
• Data freshness <24 hours
• Ingestion success rate >98%
• Event processing latency <100ms (p95)

Migration Strategy:

• Document Kafka migration path (trigger: 1000+ businesses)
• Monitor NATS/Redis Streams performance
• Prepare migration plan for scale phase

Month 6: Revenue Generation & Performance Optimization

Deliverables:

• Subscription Billing System:
  • Stripe integration
  • Free/Basic/Business/Enterprise tiers
  • Usage-based billing foundation
• Lead Fee Collection: Commission tracking for facilitated introductions
• Basic Analytics Dashboard:
  • Business resource flow analytics
  • Match success metrics
  • Environmental impact (CO₂ savings)
• Performance Optimization:
  • Query result caching (Redis)
  • Graph query optimization (Cypher profiling)
  • Materialized views for common match patterns
• Go 1.25 Features Evaluation:
  • JSON v2 performance testing (if production-ready)
  • GreenTea GC evaluation (if production-ready)
  • Fallback to Go 1.23 stable features if needed

Success Metrics:

• 30-50 paying customers (free + paid tiers) - realistic for B2B industrial SaaS
• €8k-€12k monthly recurring revenue (MRR) - conservative estimate
• Platform performance: <2s response times (p95)
• Customer satisfaction >4/5 stars
• Cache hit rate >70%
• Conversion Rate: 5-8% free-to-paid (industry average: 2-5%, exceptional: 10-15%)

Go-to-Market:

• Launch in Berlin industrial ecosystem
• Partnership agreements with utilities (data + distribution)
• Initial marketing campaign (content marketing, LinkedIn)
• Municipal dashboard pilot (1-2 cities, free for businesses, paid for cities)

Phase 3: Enterprise Features & Scale (Months 7-12) - €900k Budget

Goal: Enterprise readiness, knowledge graph integration, international expansion

Months 7-8: Advanced Platform Features & Knowledge Graph

Deliverables:

• Real-Time WebSocket Notifications:
  • Match updates, new opportunities
  • Live resource flow changes
  • Go WebSocket server (gorilla/websocket or nhooyr.io/websocket)
• Advanced Analytics and Reporting:
  • Predictive matching recommendations
  • Scenario analysis tools
  • ESG impact reporting (CSRD compliance)
• API Ecosystem Foundation:
  • REST API v1 stable
  • API documentation (OpenAPI/Swagger)
  • Webhook system for third-party integrations
  • Rate limiting and API key management
• Mobile PWA Launch:
  • Progressive Web App with offline support
  • Push notifications
  • Mobile-optimized map interface
• Knowledge Graph Integration (Phase 2 priority from architecture):
  • Semantic matching enhancement
  • Taxonomy integration (EWC, NACE codes)
  • Process compatibility matrices
  • Expected: 30-40% match quality improvement

Success Metrics:

• 150-200 active businesses (realistic growth from 30-50 paying to ~150 total)
• €25k-€40k monthly revenue (MRR) - conservative but achievable
• API adoption by 5-10 enterprises (early adopters)
• Mobile usage >20% of sessions
• Knowledge graph: 10-15% improvement in match quality (initial)

Months 9-10: Enterprise Integrations & Multi-Tenancy

Deliverables:

• GraphQL API Implementation:
  • gqlgen schema-first approach
  • Flexible querying for enterprise clients
  • Subscriptions for real-time updates
• Advanced ERP Integrations:
  • SAP (RFC, OData)
  • Oracle (REST, SOAP)
  • Microsoft Dynamics
  • Integration marketplace
• Multi-Tenancy Architecture:
  • Data isolation (schema-per-tenant or row-level security)
  • Tenant management dashboard
  • Resource usage tracking per tenant
• Advanced Security Features:
  • SOC2 compliance preparation
  • Advanced audit logging
  • Data encryption at rest and in transit
  • RBAC enhancements
• Message Queue Migration:
  • Evaluate Kafka migration if scale requires (>1000 businesses)
  • NATS → Kafka migration plan execution if triggered

Success Metrics:

• 15-25 enterprise customers (realistic for enterprise sales cycle)
• €80k-€120k monthly revenue (MRR) - B2B enterprise SaaS typically slower to scale
• Integration success rate >95%
• SOC2 Type I compliance preparation (certification takes 6-12 months)
• Multi-tenant architecture validated

Months 11-12: International Expansion & Regional Features

Deliverables:

• Multi-Language Support:
  • i18n framework (English, German, Dutch, Swedish)
  • Localized UI and content
  • Regional data formats
• Regional Data Residency:
  • EU data residency options (GDPR compliance)
  • Cross-border data transfer controls
  • Data localization settings
• International Utility Partnerships:
  • Netherlands (regional utilities)
  • Nordics (district heating networks)
  • Partnership revenue sharing model
• Market Expansion:
  • Netherlands market entry
  • Nordics pilot (Sweden, Denmark)
  • Regional regulatory compliance (country-specific)

Success Metrics:

• 300-400 total businesses across 3 countries (realistic for international expansion)
• €150k-€200k monthly revenue (MRR) - conservative growth trajectory
• 100-150% YoY growth rate (more realistic for seed stage)
• 2-3 new market entries validated (Netherlands + 1-2 Nordics)
• Regional partnerships: 3-5 utility agreements (partnerships take time to develop)

Phase 4: Scale & Optimization (Months 13-18) - €700k Budget

Goal: Full scale operations, AI-enhanced matching, profitability

Months 13-15: Advanced AI & Automation

Deliverables:

• ML-Powered Match Recommendations:
  • GraphRAG integration (Neo4j GraphRAG) for natural language queries
  • Predictive matching (anticipate resource needs)
  • Pattern recognition (recurring opportunities)
• Automated Lead Qualification:
  • Match quality scoring automation
  • Lead conversion probability prediction
  • Automated prioritization
• Predictive Analytics:
  • Resource availability forecasting
  • Demand prediction
  • Scenario analysis with Monte Carlo simulation
• Advanced Matching Algorithms:
  • Multi-party matching (3+ businesses)
  • Network optimization algorithms
  • Agent-based modeling for network simulation

Success Metrics:

• 70% improvement in match quality (vs. baseline)
• Automated lead conversion rate >40%
• Customer lifetime value increased by 25%
• GraphRAG: Natural language query support operational

Months 16-18: Full Market Penetration & Platform Maturity

Deliverables:

• Complete API Ecosystem:
  • GraphQL + REST API
  • WebSocket real-time APIs
  • White-label API access
  • Third-party developer portal
• White-Label Platform:
  • Customizable branding per tenant
  • Co-branded municipal dashboards
  • Utility partner white-label solutions
• Advanced Analytics Platform:
  • Business intelligence dashboards
  • Custom report builder
  • Data export (GDPR compliant)
  • API for analytics integration
• Strategic Partnerships:
  • Municipal partnerships (10+ cities)
  • Utility partnerships (5+ major utilities)
  • Facilitator marketplace expansion (50+ facilitators)
  • Technology partnerships (ERP vendors)

Success Metrics:

• 800-1,200 businesses registered (realistic for 18-month seed stage)
• €300k-€400k monthly revenue (MRR) - €3.6M-€4.8M ARR
• 75-80% gross margins (realistic after infrastructure costs)
• 5-8 strategic partnerships (partnerships develop slowly)
• Path to Series A validated (€3M+ ARR typically needed for Series A)

Critical Path Dependencies

Technical Dependencies

1. Data Quality → Matching Accuracy → User Adoption
2. Performance → Scalability → Enterprise Adoption
3. Security → Trust → Large Customer Acquisition
4. Graph Database Setup → Matching Engine → MVP Launch
5. Go 1.25 Stack → Backend Performance → User Experience
6. Knowledge Graph Integration → Match Quality → Enterprise Value
7. Event-Driven Architecture → Real-Time Features → User Engagement

Business Dependencies

1. Seed Data → Initial Matches → User Validation
2. Utility Partnerships → Data Access → Market Reach
3. First Customers → Case Studies → Market Momentum
4. Service Marketplace → Regular Engagement → Network Effects
5. Municipal Partnerships → Free Business Access → Network Growth

Risk Mitigation Milestones

Monthly Risk Reviews

• Technical Risks: Performance, security, scalability, Go 1.25 experimental feature availability
• Market Risks: Adoption, competition, regulation, cold start problem
• Financial Risks: Burn rate, revenue projections, CAC/LTV ratio
• Data Risks: Data quality, privacy compliance, GDPR adherence

Pivot Triggers (Revised with Realistic Targets)

• Month 3: <10 businesses registered → Pivot to different market or vertical
• Month 6: <€5k MRR (€60k ARR run rate) → Focus on enterprise sales, adjust pricing
• Month 9: <€15k MRR (€180k ARR run rate) → Restructure business model, evaluate partnerships
• Month 12: <€30k MRR (€360k ARR run rate) → Pivot to municipal/utility-focused model
• Month 18: <€50k MRR (€600k ARR run rate) → Consider seed extension or pivot strategy

Early Warning Signals

• Week 4: <20 businesses signed up for pilot → Accelerate seed data collection
• Month 4: <40% data completion rate → Simplify onboarding, add support
• Month 6: No implemented connections → Focus on low-capex matches
• Month 6: Conversion rate <3% (free-to-paid) → Improve value proposition, pricing
• Month 8: CAC > 3x monthly revenue per customer → Reduce marketing spend, improve conversion
• Month 9: Churn rate >10% monthly → Address product-market fit issues

Resource Allocation

Engineering Team (60% of budget)

• Backend Engineers (4):
  • Go 1.25 APIs, matching engine, graph database
  • Event-driven architecture, message queue integration
  • Economic calculator, plugin architecture
• Frontend Engineers (2):
  • React + Next.js, Mapbox visualization
  • PWA development, real-time WebSocket UI
• DevOps Engineer (1):
  • Kubernetes infrastructure, CI/CD pipelines
  • Monitoring (Prometheus, Grafana), infrastructure automation
• Data Engineer (1):
  • Data pipelines, ETL, analytics
  • Knowledge graph integration, ML model deployment

Business Team (20% of budget)

• Business Development (1 person):
  • Utility partnerships, municipal sales
  • Channel partner development
• Domain Experts (2 people):
  • Industrial symbiosis facilitation
  • Regulatory compliance (EU, country-specific)
• Operations/Customer Success (1 person):
  • Customer onboarding, support
  • Facilitator marketplace management

Infrastructure & Tools (20% of budget)

Note: Infrastructure costs scale with usage. Below are peak estimates for Month 18.

Cloud Costs (scaling from Month 1 to Month 18):

• Month 1-6: €2k-€5k/month (development, MVP scale: 50-100 businesses)
  • AWS/GCP: €1.5k-€3k/month (EKS/GKE, managed databases small instances)
  • Neo4j: €500-€1k/month (Community or small Enterprise)
  • PostgreSQL RDS: €300-€500/month (small instances)
  • Redis: €200-€400/month (small cache)
• Month 7-12: €5k-€10k/month (growth phase: 200-400 businesses)
  • AWS/GCP: €3k-€6k/month
  • Neo4j: €1k-€2k/month
  • PostgreSQL RDS: €500-€1k/month
  • Redis: €400-€800/month
• Month 13-18: €10k-€15k/month (scale phase: 800-1,200 businesses)
  • AWS/GCP: €6k-€9k/month
  • Neo4j: €2k-€3k/month (Enterprise scaling)
  • PostgreSQL RDS: €1k-€2k/month
  • Redis: €800-€1.5k/month

Third-party Services:

• Monitoring (Datadog/New Relic): €500-€2k/month (scales with infrastructure)
• Security (Vault, secrets management): €200-€500/month
• Payments (Stripe): Transaction-based (typically 2.9% + €0.30 per transaction)
• Mapbox: €0 (free tier: 50k loads/month), then €200-€500/month at scale

Development Tools:

• GitHub Enterprise: €4/user/month (or GitHub Pro at €4/user/month)
• IDEs: €100-€200/month (JetBrains licenses, etc.)
• CI/CD: Included in GitHub or €50-€200/month (CircleCI, etc.)
• Artifact Repositories: €50-€100/month

Total Infrastructure Costs (18 months):

• Conservative Estimate: €120k-€180k (assumes gradual scaling)
• Realistic Peak: €180k-€270k (if growth exceeds expectations)

Success Metrics Dashboard

Daily Metrics

• Active users, API calls, error rates
• Match generation, user engagement
• Revenue, customer acquisition

Weekly Metrics

• Customer satisfaction, feature usage
• Performance benchmarks, uptime
• Market feedback, competitor analysis

Monthly Metrics

• Revenue growth, customer retention
• Market expansion, partnership progress
• Technical debt, code quality
• Team productivity, burn rate

Exit Strategy Milestones

Year 1: Product-Market Fit (Realistic Targets)

• 50-100 paying customers (conservative but achievable for B2B industrial SaaS)
• €300k-€600k total revenue (€250k-€500k ARR) - realistic for seed stage first year
• Clear unit economics (LTV/CAC ratio >3-5x target, 70x would be exceptional)
• Validated market demand and willingness to pay
• 3-5 implemented connections proving ROI
• Service marketplace operational (basic version)

Note: Most seed-stage B2B SaaS companies take 12-18 months to reach €500k ARR. €2M ARR in Year 1 would be exceptional (top 5% of startups).

Year 2: Scale Validation (If Product-Market Fit Achieved)

• 200-400 customers (growth from proven model)
• €1.5M-€3M total revenue (€1.2M-€2.5M ARR) - 4-5x growth if PMF achieved
• International presence (2-3 countries)
• Operational excellence and repeatable processes
• 5-8 utility partnerships (realistic timeline)
• Knowledge graph showing measurable match quality improvement

Year 3: Exit Preparation (If Scale Validated)

• 600-1,000 customers (realistic growth trajectory)
• €4M-€6M total revenue (€3.5M-€5M ARR) - Series A territory
• 75-80% gross margins, approaching profitability
• Strategic partnerships (utilities, municipalities, ERP vendors)
• Competitive moat established (network effects, data accumulation)
• Ready for Series A fundraising (€3M+ ARR typically minimum)

Contingency Plans

Technical Failure Scenarios

• Database Performance: Fallback to simplified matching
• API Downtime: Cached responses, maintenance pages
• Data Loss: Comprehensive backups, recovery procedures

Business Failure Scenarios

• Low Adoption: Pivot to enterprise-focused model
• Competition: Differentiate through partnerships
• Regulatory Changes: Adapt compliance requirements

Financial Failure Scenarios

• Slow Revenue: Extend runway through strategic partnerships
• High Burn Rate: Reduce scope, focus on core features
• Funding Delay: Bootstrap through early revenue

---

Implementation Timeline Visualization

Month 1-3: Foundation & MVP
├── Team & Infra Setup (Go 1.25, Neo4j, NATS/Redis)
├── Data Architecture (Graph + Spatial)
├── Heat Matching MVP (manual entry)
└── Pilot Launch (Berlin industrial + hospitality)

Month 4-6: Expansion & Revenue
├── Multi-Resource Support (water, waste)
├── Service Marketplace MVP
├── Automated Ingestion (ERP, IoT)
└── Revenue Generation (subscriptions, leads)

Month 7-12: Enterprise & Scale
├── Knowledge Graph Integration
├── Advanced Features (WebSocket, analytics)
├── Enterprise Integrations (GraphQL, ERP)
├── Message Queue Migration (Kafka if needed)
└── International Expansion (Netherlands, Nordics)

Month 13-18: AI & Market Penetration
├── ML/AI Features (GraphRAG, predictive)
├── White-Label Platform
└── Strategic Partnerships

Technology Evolution Timeline

MVP Phase (Months 1-6)

• Message Queue: NATS or Redis Streams
• Go Version: 1.25 with feature flags (fallback to 1.23)
• Graph DB: Neo4j Community/Enterprise
• Deployment: Kubernetes (EKS/GKE)

Scale Phase (Months 7-12)

• Message Queue: Evaluate Kafka migration (trigger: 1000+ businesses)
• Go Version: 1.25 stable features, evaluate experimental (JSON v2, GreenTea GC)
• Graph DB: Neo4j Enterprise (scaling), consider TigerGraph evaluation
• Knowledge Graph: Phase 2 implementation

Enterprise Phase (Months 13-18)

• Message Queue: Kafka if scale requires
• Go Version: Latest stable with production-ready experimental features
• Graph DB: Neo4j Enterprise or TigerGraph at scale
• AI/ML: GraphRAG, predictive analytics operational

Total Timeline: 18 months to product-market fit validation Total Budget: €2.5M seed funding Success Criteria (Revised - Realistic):

• 800-1,200 businesses registered (vs. optimistic 5,000)
• €3.6M-€4.8M ARR (€300k-€400k MRR) vs. optimistic €21M ARR
• 75-80% gross margins (vs. optimistic 82%)
• Series A readiness (€3M+ ARR typically required) vs. IPO-readiness

Realistic Growth Path:

• Month 6: €8k-€12k MRR (€100k-€150k ARR run rate)
• Month 12: €25k-€40k MRR (€300k-€480k ARR run rate)
• Month 18: €50k-€80k MRR (€600k-€960k ARR run rate)

Note: The original projections (€21M ARR Year 3, 5,000 customers) would place Turash in the top 1% of B2B SaaS startups. The revised projections are more realistic for seed-stage companies while still being ambitious. Exceptional performance could exceed these targets.