> For the complete documentation index, see [llms.txt](https://docs.glbnxt.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.glbnxt.com/platform/overview/introduction.md). # Introduction ### What is GLBNXT Platform? GLBNXT Platform is a fully managed AI platform built for development teams. It delivers the world's best open-source tools for model serving, vector databases, workflow automation, and frontend interfaces in a single enterprise-ready environment that is sovereign, secure, and orchestrated end-to-end. Most organisations building with AI are slowed down by fragmented stacks, broken integrations, and the operational burden of managing open-source infrastructure themselves. GLBNXT Platform removes that complexity. Compute, networking, orchestration, secrets management, observability, compliance, and model routing are all handled at the platform level, so your engineers focus entirely on building AI products rather than maintaining the stack beneath them. ### How It Works The platform is structured around a clear division of responsibility between GLBNXT and your development team. **What GLBNXT manages:** * GPU and CPU compute * Kubernetes orchestration * Networking and security * Storage and backups * Secrets and credential vault * Observability and monitoring * Compliance and audit trails * Model routing **What your team builds:** * AI assistants and chat interfaces * RAG pipelines and knowledge systems * Multi-agent workflows and automation * APIs and functions * User-facing applications A single platform orchestrates the layer beneath it all, so every component works together by default. No glue code, no infrastructure incidents, no time lost on DevOps. ### Platform vs. Workspace GLBNXT offers two distinct products serving different audiences within an organisation. **GLBNXT Platform** is designed for development teams. It provides the full AI infrastructure stack for engineers, data scientists, and architects who build and deploy AI-powered applications and services. **GLBNXT Workspace** is designed for knowledge workers and business teams. It provides a governed, compliant AI environment for employees who use AI to do their work, without needing to build anything. Both products share the same commitment to European data sovereignty and enterprise-grade compliance. If you are looking for documentation on GLBNXT Workspace, visit the Workspace documentation. ### Key Principles **Sovereign by design.** All compute runs within the EU. There are no dependencies on US hyperscaler infrastructure, with full data residency guarantees for every deployment. **Open, not locked in.** The platform is built on open-source technologies and open standards. No proprietary layers, no black-box components, and no lock-in to a single vendor or model provider. **Managed, not DIY.** GLBNXT handles every operational concern so your team does not have to. Infrastructure, security, compliance, and orchestration are platform responsibilities, not yours. **Built for builders.** Every capability on the platform is designed to reduce time-to-value for development teams. From prototype to production in days, not months. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.glbnxt.com/platform/overview/introduction.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.