Autonomous Multi-Agent Intelligence for Renewable Energy Markets
The €350B European renewable energy sector suffers from inefficient market intelligence: mid-sized developers miss €50M+ opportunities because existing solutions are too slow, expensive, or lack domain specialization. Consultants cost €20K+ per project and take weeks. Data platforms require manual searching. Generic AI chatbots produce superficial answers without verified domain data. Developers cannot continuously monitor 15+ markets. Greenvi AI is developing an Autonomous Multi-Agent Intelligence System operating 24/7 without user input, replacing weeks of analysis with minutes of expert-level automated intelligence. Six specialized agent teams enhanced with access to 50K+ renewable energy documents and 25K+ entity knowledge graph: Market-Prediction (auction timing ±2 months, price ±10%), Market-Strategy (go-to-market roadmaps), Business-Planning (financial models), plus three supporting teams. The system monitors continuously, coordinates teams via proprietary orchestration logic, and proactively alerts users with integrated recommendations. From TRL 4 (lab-validated prototypes) to TRL 6+ through: (1) autonomous operation (99% uptime), (2) 85% accuracy vs. expert baseline (100+ scenarios, statistical validation), (3) multi-agent orchestration (95% success), (4) 5 pilots over 12+ months. Novel validation framework comparing AI against 3-5 expert analysts. Technical: Domain-specific context engineering via RAG, knowledge graph (25K+ entities), continuous learning, event-driven microservices. Innovation: orchestration enabling autonomous coordination across teams, shared context Team: 10+ years renewable energy expertise (World Bank, OECD, Blymyer), 75% working together, pilot access via ASEU and UABIO. Targets: €500K+ ARR, 3+ LOIs. Impact: EU Green Deal acceleration (weeks to minutes), 8 Ukrainian deep tech jobs (later 50+), autonomous market intelligence category creation, TRL methodology for domain-specific multi-agent AI. **Character count:** 1,746 ✅ (2
4/6 complete
Imported from OpenAIRE Ukraine | Grant 101311585.
Open sourceOverview
Grant agreement 101311585 | GREENVI-AI
Open sourceThe €350B European renewable energy sector suffers from inefficient market intelligence: mid-sized developers miss €50M+ opportunities because existing solutions are too slow, expensive, or lack domain specialization. Consultants cost €20K+ per project and take weeks. Data platforms require manual searching. Generic AI chatbots produce superficial answers without verified domain data. Developers cannot continuously monitor 15+ markets. Greenvi AI is developing an Autonomous Multi-Agent Intelligence System operating 24/7 without user input, replacing weeks of analysis with minutes of expert-level automated intelligence. Six specialized agent teams enhanced with access to 50K+ renewable energy documents and 25K+ entity knowledge graph: Market-Prediction (auction timing ±2 months, price ±10%), Market-Strategy (go-to-market roadmaps), Business-Planning (financial models), plus three supporting teams. The system monitors continuously, coordinates teams via proprietary orchestration logic, and proactively alerts users with integrated recommendations. From TRL 4 (lab-validated prototypes) to TRL 6+ through: (1) autonomous operation (99% uptime), (2) 85% accuracy vs. expert baseline (100+ scenarios, statistical validation), (3) multi-agent orchestration (95% success), (4) 5 pilots over 12+ months. Novel validation framework comparing AI against 3-5 expert analysts. Technical: Domain-specific context engineering via RAG, knowledge graph (25K+ entities), continuous learning, event-driven microservices. Innovation: orchestration enabling autonomous coordination across teams, shared context Team: 10+ years renewable energy expertise (World Bank, OECD, Blymyer), 75% working together, pilot access via ASEU and UABIO. Targets: €500K+ ARR, 3+ LOIs. Impact: EU Green Deal acceleration (weeks to minutes), 8 Ukrainian deep tech jobs (later 50+), autonomous market intelligence category creation, TRL methodology for domain-specific multi-agent AI. **Character count:** 1,746 ✅ (2