The PDGS serves as the bridge between the raw data collected by onboard instruments and the end users who rely on that data for various applications, such as scientific research, environmental monitoring, and mission planning. 

• Payload processing and generation of data products
• Integration with ground stations and mission control
• Scalable processing supporting multiple mission needs
• Quality assessment and dissemination

EO Data Platform is a cloud-based environment designed to enable efficient access, processing, and analysis of Earth Observation (EO) data. By bringing processing closer to the data, the platform significantly reduces data transfer requirements and accelerates the development of data-driven solutions.

• Processing close to the data
• Lower data transfer overhead
• Faster development and deployment of EO services
• One environment for testing, analytics, and operations

GeoAI delivers operational AI services for satellite data, centered on generating satellite imagery embeddings for various models, detecting anomalies in time series of embeddings, and providing curated AI-ready datasets for model training and evaluation. 

• Embeddings for satellite imagery 
• Anomaly detection in time-series
• AI-based cloud masks 
• Integrated workflows without unnecessary data export

CloudFerro Data Gateway is a CloudFerro portfolio of services designed to help external Earth Observation (EO) Data Providers onboard, publish, and manage datasets within the CloudFerro ecosystem.

• Dataset hosting in the CloudFerro repository
• Integration with provider-owned APIs
• Managed catalogue and discovery services
• A model aligned with the provider's architecture and governance requirements

Long Term Archive (LTA) is a cloud-based system designed to securely store and provide access to large volumes of Earth Observation data collected by satellite missions. 

• Secure storage for large EO datasets
• Unified access to historical satellite data
• Reliable long-term data preservation
• High-performance retrieval for diverse users

EO User Segment is a comprehensive service layer that enables users to efficiently access, manage, and exploit Earth Observation (EO) data and services. It provides a unified interface for data discovery, tasking requests, identity and access management, and integration through standard web services and APIs.

• Unified access to EO data and services
• Data discovery, tasking, and access management
• Standard APIs for seamless system integration
• Supports public, research, and commercial users

EO Traceability Service is a cloud-based solution that enables end-to-end tracking of Earth Observation data products, processors or AI/ML models throughout their lifecycle.

• End-to-end tracking of EO data & services lifecycle
• Records creation, processing, and distribution events
• Ensures data integrity, authenticity, and provenance
• Builds trust in EO data usage

The METEO Data Platform is a sovereign, cloud-powered solution that transforms how meteorological data is managed, accessed, and used. It provides a secure, unified environment to integrate, store, and distribute data from multiple sources - such as satellites, radar, ground stations, and models - while ensuring full control and data sovereignty. 

• Sovereign cloud platform for unified METEO data management
• Scalable infrastructure for fast analytics
• Integrated environment for AI and forecasting
• Enhanced decision-making with early warning capabilities

The Long Term Archive (LTA) is a cloud-based solution designed for secure, scalable, and cost-effective storage of large volumes of meteorological data from multiple sources. It provides unified access to historical datasets while ensuring reliable long-term preservation, making it ideal for research, climate analysis, model validation, and compliance use cases. 

• Long-term storage for historical datasets
• Cost optimization for less frequent access
• Support for climate analytics and model validation
• Reliable preservation of high-value data

MeteoAI delivers operational AI services for meteorological data, focusing on the generation of model embeddings, anomaly detection in embedding time series, and the provision of curated, AI-ready datasets for training and evaluation.

• Operational AI for meteorological data analysis
• Embeddings and anomaly detection capabilities
• HPC infrastructure for AI/ML workflows
• Curated datasets for training and evaluation

The METEO Mobile Center is a fully sovereign, secure, and self-contained solution designed for flexible deployment in the field. Delivered as a mobile, container-based unit, it supports storm chasers, rapid response teams, and other mission-critical operations requiring immediate access to meteorological data, processing capabilities, and communication systems in remote or dynamic environments. 

• Mobile, container-based METEO deployment unit
• Sovereign and secure field operations environment
• Rapid deployment without fixed infrastructure
• No building requirements, saving office space

The solution is designed to support armed forces operations in air-gapped, disconnected, and low-connectivity environments. It enables secure and continuous data exchange and coordination across infrastructure elements, supporting faster and more resilient multi-domain operations. 

• Ready for air-gapped and disconnected environments
• Modular, rapidly deployable private cloud architecture in a container
• Local data processing and storage

A solution designed for the defence and security sector, ensuring full control over infrastructure, data, and administrative access. It undergoes security accreditation for ICT systems in accordance with defence ministry regulations and NATO standards.

• Reinforced security architecture
• Isolated private cloud for sensitive environments
• TEMPEST-compliant container
• Full control over access, logs, and operations

Multi-domain operational clouds (MDOC) provide a conceptual framework for integrating diverse information spaces to enable decision advantage and support multi-domain command and control. To fulfill this role, military cloud infrastructure and its supporting network must be capable of self-organization, self-healing, graceful degradation under adverse conditions, and maintaining appropriate levels of redundancy.

• Architecture for interoperable multi-domain operations
• Ready for operation in disconnected environments
• Leverages AI and advanced analytics
• Supports situational awareness and decision-making