Geofuchs - Hydra 8 - AI Team Roadmap
This roadmap presents a research-based productivity model for the Geofuchs AI development project, spanning 2026-2028. Using realistic hour estimates derived from published AI coding productivity research with structured workflows, we demonstrate how a five-agent AI team can deliver a comprehensive geospatial intelligence platform whilst achieving significant cost savings.
Research Foundation
Evidence-Based Productivity Model
Our roadmap is grounded in peer-reviewed research and real-world case studies from leading AI organisations. These studies demonstrate that structured AI workflows with proper context engineering significantly outperform ad-hoc approaches.
METR RCT Study (July 2025)
Ad-hoc AI implementation resulted in 19% slower development with less than 44% code acceptance rates.
Anthropic Internal Research (August 2025)
Structured workflows achieved 67% more pull requests and 50% productivity gains compared to baseline.
Context Engineering Best Practices
Anthropic's engineering guidelines demonstrate how proper context management maximises AI agent effectiveness.
GitHub Spec-Driven Development
Planning specifications before implementation reduces rework and improves code quality significantly.
Revised Forecasts Analysis
Anthropic cut productivity estimates in half after analysing Claude's real-world failure rates, informing our conservative approach.
Why Structured Workflows Succeed for Geofuchs
Four Critical Success Factors
Our structured approach leverages specific advantages that maximise AI productivity whilst minimising common pitfalls identified in research.
- Pre-planned tasks: ROADMAP.md provides detailed specifications, eliminating ambiguity
- Context engineering: CLAUDE.md, skills documentation, and project context enable informed decisions
- Greenfield advantage: Year 1 development has no legacy constraints or technical debt
- Structured review: Five parallel agents allow comprehensive review cycles and quality assurance
Productivity Analysis
AI Multipliers Across Three Years
Our productivity model applies conservative multipliers that increase as the codebase matures and AI agents gain domain expertise. Each year includes a 15% oversight buffer for human review and quality assurance.
0.46x
Year 1 Effective Rate
Greenfield development with 0.40x base multiplier plus 15% oversight equals 54% time savings
0.575x
Year 2 Effective Rate
Growing codebase with 0.50x base multiplier plus 15% oversight equals 42% time savings
0.69x
Year 3 Effective Rate
Mature system with 0.60x base multiplier plus 15% oversight equals 31% time savings
Team Setup and Model Allocation
Service Configuration
3-Year Total: £36,720 (~€34,000)
Model Distribution Strategy
75%
Sonnet 4
Implementation, coding, testing, and debugging tasks
15%
Opus 4
Architecture, design decisions, and specification work
10%
Perplexity
API research, library evaluation, and best practices
Meet the Five-Agent Team
Each agent specialises in different aspects of the platform, working in parallel to maximise throughput whilst maintaining code quality through cross-review.
🔴 Alpha Agent
Infrastructure, migration, and core system architecture
🟠 Beta Agent
Data processing pipelines and integration layers
🟡 Gamma Agent
External service integration and API development
🟢 Delta Agent
Machine learning models and processing algorithms
🔵 Epsilon Agent
User interface, tooling, and developer experience
Year 1: 2026
Foundation and Remote Sensing
Year 1 establishes the core infrastructure and remote sensing capabilities. With a 0.46x effective rate, we achieve 54% time savings whilst building the foundation for advanced geospatial intelligence.
1
Q1: Infrastructure Migration
GEX131 migration, GDAL/Rasterio stack, Sentinel Hub integration, and tile processing pipeline
405 hours (880 original)
2
Q2: SAM Integration
SAMGeo integration, prompt engineering, mask post-processing, and memory systems
387 hours (840 original)
3
Q3: TorchGeo Models
Pre-trained models, inference service, training pipeline, and supervisor logic
423 hours (920 original)
4
Q4: Vision Pipeline
CLIP/SigLIP integration, image understanding, sub-agents, and visualisation UI
424 hours (924 original)
Year 1 Agent Workload Distribution
The five agents work in parallel across quarterly milestones, with workload balanced to prevent bottlenecks whilst maintaining quality through cross-review.
1,639
Total AI-Assisted Hours
Year 1 development time with AI assistance
3,560
Original Estimate
Traditional development hours required
1,921
Hours Saved
54% reduction in development time
Year 2: 2027
Learning, Agents and Knowledge
Year 2 introduces advanced learning systems, autonomous agents, and V2X capabilities. With a 0.575x effective rate, we achieve 42% time savings whilst expanding platform intelligence.
1
Q1: GraphRAG Foundation
Feedback loops, data curation, Microsoft GraphRAG integration, entity extraction, and GeoSPARQL ontology
2
Q2: Active Learning & MCP
LoRA training, confidence scoring, MCP protocol implementation, tool adapters, and V2X foundation
3
Q3: Autonomous Agents
Reflection module, DSPy optimiser, autonomous planning, workflow agents, and per-agent memory
4
Q4: Goal Alignment & Analysis
Constitution layer, spatial analysis suite, V2X northbound interface, and security implementation
Year 2 Quarterly Breakdown
Core Platform Development
01
Q1: GraphRAG & Feedback (529 hrs)
Knowledge graph integration and learning systems
02
Q2: MCP Toolkit (529 hrs)
Model Context Protocol and tool ecosystem
03
Q3: Autonomous Agents (575 hrs)
Self-directed planning and workflow automation
04
Q4: AI Spatial Analysis (552 hrs)
Advanced geospatial intelligence capabilities
V2X Development Track
01
Q2-Q3: V2X Foundation (322 hrs)
MQTT broker, CARLA integration, CCMA algorithm, and C-ITS agent
02
Q4: Northbound & Security (276 hrs)
AMQP 1.0 interface, EU CCMS PKI, H3 indexing, and CAM/DENM generation
2,783
Year 2 AI Hours
4,840
Original Estimate
2,057
Hours Saved (42%)
Year 3: 2028
Scale, Trust and Generation
Year 3 focuses on enterprise scalability, explainability, and advanced generation capabilities. With a 0.69x effective rate, we achieve 31% time savings whilst preparing for production deployment.
Q1: XAI & Compliance
Explainability dashboard, source attribution, GDPR compliance, data lineage, authentication, and NordicWay V2X pilot
882 hours (1,280 original)
Q2: Guardrails & Generation
NeMo guardrails, conflict resolution, vector-to-image generation, image transforms, and V2X production hardening
827 hours (1,200 original)
Q3: Model Distillation
Model distillation, deployment, simulation sandbox, synthetic data generation, and benchmarking
690 hours (1,000 original)
Q4: Enterprise Scale
Multi-region DR, GPU autoscaling, edge caching, and three industry verticals
910 hours (1,320 original)
Year 3 Industry Vertical Expansion
The final quarter of Year 3 delivers three specialised industry verticals, demonstrating the platform's versatility across diverse geospatial intelligence applications.
Urban Planning Vertical
Comprehensive tools for city development, zoning analysis, infrastructure planning, and smart city initiatives
Infrastructure Vertical
Asset management, condition monitoring, predictive maintenance, and lifecycle analysis for critical infrastructure
Environmental Vertical
Climate monitoring, biodiversity tracking, deforestation detection, and environmental impact assessment
3,309
Year 3 AI Hours
4,800
Original Estimate
1,491
Hours Saved (31%)
Three-Year Grand Total Analysis
Across three years of development, the AI-assisted approach delivers substantial time and cost savings whilst maintaining high quality through structured workflows and comprehensive oversight.
Total Development Hours
Original: 13,200 hours
AI-Assisted: 7,731 hours
Savings: 5,469 hours (41%)
Weekly Capacity
Original: ~85 hrs/week
AI-Assisted: ~50 hrs/week
Per Agent: ~10 hrs/week
Model Distribution
Opus 4: 1,160 hrs (15%)
Sonnet 4: 5,798 hrs (75%)
Perplexity: 773 hrs (10%)
Financial Analysis and ROI
The AI-assisted approach delivers exceptional return on investment, with substantial cost savings that far exceed the technology investment required.
Traditional Development Cost
13,200 hours × €75/hr = €990,000
AI-Assisted Development
7,731 hours × €75/hr = €579,825
AI Team Cost: €34,000
Total: €613,825
Total Savings
€376,175 saved
38% cost reduction
ROI: 1,107%
(€12 saved per €1 spent)
Value Proposition: For every €1 invested in AI tooling (€34,000 total), the project saves €12 in development costs (€410,175 in labour savings), delivering an extraordinary 1,107% return on investment over three years.
Agent Workload Summary and Next Steps
The three-year roadmap distributes work evenly across five specialised agents, ensuring balanced workloads and comprehensive platform coverage.
1
Track Progress
Use checkboxes in quarterly tables to mark completed tasks: [ ] → [x]
2
Monitor Status
Apply status indicators: [ ] Not started, [~] In progress, [x] Completed, [!] Blocked
3
Review Quarterly
Assess progress, adjust estimates, and refine approach based on actual performance
4
Maintain Documentation
Keep ROADMAP.md, CLAUDE.md, and context files updated for optimal AI performance
This roadmap demonstrates how structured AI workflows, grounded in research and real-world evidence, can deliver a sophisticated geospatial intelligence platform with 41% time savings and 38% cost reduction whilst maintaining high quality standards.