The Open Source Discovery Point showcases open-source components developed within the IPCEI-CIS ecosystem. This tool provides visibility into OSS components functionalities, repositories, licences, and architecture domains across participating initiatives, supporting interoperability, collaboration, and knowledge sharing within the European cloud-edge ecosystem.

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Open Micro Frontend Platform

The Open Micro Frontend Platform (OpenMFP) provides an opinionated platform for building portals and complex web-based applications with enterprise qualities.
  • Dynamic Micro Frontend Extension Model: Self-sufficient micro frontend applications run within other applications, with a shared, dynamic navigation layer (header, footer, menu).
  • Central Shared Services: Provides central shared services for authentication, authorization, reducing redundancy across all integrated micro frontends.
  • Seamless Multi-Team Integration: Enables seamless integration of UI capabilities from different teams and disparate organizations while maintaining team independence.
  • Independent Release Cadence: Untangles monolithic frontend build and planning processes into independent releases, splitting applications into smaller, autonomously deployable chunks.
  • Reuse of Shared Infrastructure: Supports the reuse of shared functionality and infrastructure, and is built on the Luigi micro frontend framework.

Open Resource Discovery

ORD is an open protocol for publishing and discovering application and service metadata. It defines a structured schema for APIs, events, data products, and AI agents, enabling consistent discovery and integration across systems and marketplaces. As the foundation of the Data Fabric, ORD metadata is collected by UMS and used by Knowledge Graph to map relationships across providers.
  • Common Metadata Language: Defines a clear, machine-readable format for describing APIs, events, data products, and related resources, ensuring consistent structure across all producers.
  • Improved Resource Discoverability: Standardizes key details like endpoints, capabilities, and ownership, making resources easier to find and understand across distributed landscapes.
  • Cross-System Interoperability: One shared specification lets platforms and tools exchange and interpret resource descriptions without custom integrations.
  • Extensible Schema: Supports safe extensions so organizations can add domain-specific fields without breaking compatibility, allowing flexibility for future needs.

Perses

An open-source dashboard tool for visualizing observability data with an open specification designed for portability across various datasources such as Prometheus, OpenSearch, Jaeger, etc.
  • Open Dashboard Specification: Provides an open specification for dashboards, ensuring portability and avoiding vendor lock-in across observability backends.
  • Multi-Source Observability: Visualizes data from Prometheus, Tempo, Loki, and Pyroscope in a single platform, reducing the need for separate dashboarding tools.
  • Dashboard-as-Code: SDK-driven dashboard-as-code capabilities enable version-controlled, reproducible dashboard definitions.
  • Kubernetes Native: Kubernetes operator integration enables GitOps-based deployment and lifecycle management of dashboards alongside the applications they monitor.

Platform Mesh

Platform Mesh is the main Platform API for users and technical services to order and orchestrate capabilities attached to the environment. Its design principle is inherited from the Kubernetes Resource Model (KRM), enabling declarative management of services across distributed environments.
  • Multi-tenant Control Planes: Supports complex multi-tenant scenarios without compromising security and provides a foundation for a scalable and regionally distributed service ecosystem.
  • KRM-based API Management: KRM as the "lingua-franca" for declarative service management. Control Planes provide a declarative API layer between providers and consumers.
  • Service Provider Integration: Seamless provider integration through combination points between control planes of service providers and service consumers.
  • Decentralized Marketplace Support: Export and Binding interfaces that back decentralized marketplaces for consumers to browse available APIs and providers to publish services.

Thalamus

Thalamus is an open-source AI-as-a-Service platform that transforms any AI infrastructure into scalable, consumable AI services. Built for sovereign AI environments, it enables organizations to deploy, operate, and consume AI securely, compliantly, and independently of hyperscale cloud providers.
  • Sovereign & Regulatory-Compliant AI: Designed for sovereign AI ecosystems requiring restricted, confidential, and controlled deployments. Complies with regulatory requirements for the public sector and highly regulated industries.
  • Infrastructure Freedom: Runs on any AI infrastructure with out-of-the-box support for bare-metal AI hardware. Built for GenAI inference, machine learning, and emerging AI applications. Delivers Model-as-a-Service capabilities for both open-weight and proprietary models.
  • Confidential AI by Design: Enables confidential computing to protect model weights, intellectual property, and sensitive data.
  • Optimized AI Operations: Advanced routing, scheduling, and caching maximize performance, utilization, and cost efficiency for AI inference. Built-in observability, monitoring, metrics, lifecycle management, and model provisioning.
  • Open Standards, No Vendor Lock-In: Built on open, standardized Kubernetes APIs in collaboration with the open-source community. Vendor-specific inference blueprints are contributed back to open source and the Linux Foundation.

Unified Metadata Service

UMS consolidates fragmented metadata - APIs, events, services, and tenant information - across complex landscapes into a unified, extensible, model-driven environment. It enables marketplaces with consistent, searchable service catalogs and allows AI systems to discover and orchestrate resources automatically. Together with ORD, UMS enables seamless integration between services without manual pre-design.

Natural Language Processing

A lightweight Python component that performs Named Entity Recognition (NER) on input text using a Villanova LLM (or any OpenAI-compatible chat model) via an OpenAI-compatible API.

Citizen Feedback Analysis

Citizen Feedback Analysis is a predefined module of the Villanova LCNC Platform, designed to automatically analyze free-text citizen feedback and classify it into a public service category and a sentiment label using a Large Language Model (LLM).The module is built for low-code/no-code environments and is already installed and configured within the Villanova Platform.

Video Synthesis

Video anonymization system designed for privacy compliance. Automatically detects and anonymizes sensitive information in video content using state-of-the-art deep learning models. Detection Targets:  Human faces , Vehicle license plates

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