Deep and Green Tech for Sustainable Development

Green Iceberg modernises deep-tech and green-tech capability through applied R&D, curriculum updates and practical pilots aligned with local, regional and European priorities. We support delivery of the European Green Deal and the Digital Transition by applying AI, IoT, cybersecurity and digital twins—and, where useful, distributed ledger approaches—to real-world industrial and public-value use cases. Our focus is on systems that are auditable, resilient and governance-ready—especially where safety, compliance and critical infrastructure are involved.

• Capability building in AI, IoT, cybersecurity and digital twins for industry and public services (including executive-ready and curriculum renewal formats)
• Data readiness assessment and requirements definition (including governance and assurance needs)
• Digital twin pilots for assets, infrastructure and operations (e.g., wind turbine monitoring, forecasting and optimisation)
• Time-series forecasting delivered in Python-ready form for energy management (monitoring, cost forecasting, settlement under dynamic tariffs)
• Pilot design, validation planning and measurement frameworks for performance and impact (including transparency and explainability needs where AI is used)
• Implementation pathway support: integration planning, operating routines and skills transfer
• AI-enabled cybersecurity pilots and assessments (e.g., adversarial risk checks, resilience measures, and compliance-oriented monitoring)

We combine technical delivery with policy and programme readiness. Each engagement moves from clear requirements and data assessment to model/system design, rapid prototyping and validation against measurable indicators. Every pilot includes an implementation route aligned with public-sector expectations—covering integration, governance, information assurance and the capabilities needed for long-term operation, including risk controls for AI-enabled systems and clarity on accountability and oversight.

• Requirements pack, architecture and integration blueprint (including data flows)
• Working prototype with validation report and performance measures
• Indicator framework covering accuracy, reliability, cost, service quality and sustainability impact (and, where relevant, resilience and compliance indicators)
• Information assurance approach and risk controls appropriate to context (including adversarial and misuse considerations for AI-enabled components)
• Implementation roadmap with phases, resourcing, operating model and capability needs