# adamatics > The AI platform your whole organization actually deploys. The Adamatics Platform is the governed home for everything your teams build with AI: it catalogs, runs, and governs the AI-generated artifacts your teams build — apps, agents, notebooks and models — inside your own infrastructure. ## Pages - [Platform](https://www.adamatics.com/platform/): Adamatics Platform catalogs, runs, and governs the AI-generated artifacts and models your teams build, inside your own infrastructure. - [Pricing](https://www.adamatics.com/pricing/): Adamatics' platform is licensed as one flat annual fee covering every employee. No per-seat pricing, no usage caps. The number depends on company size. - [FAQ](https://www.adamatics.com/faq/): Direct answers to the questions we get: where your data lives, how deployment works, what it costs, and whether you could just build it yourself instead. - [About](https://www.adamatics.com/about/): Adamatics is a Copenhagen-based software company. Since 2018 we have built governed AI and data infrastructure for finance, pharma, and research teams. - [Contact](https://www.adamatics.com/contact/): Book a 30-minute call with the Adamatics team. Walk out with a map of what's already running in your company and an honest answer on whether you need us. ## Blog - [AI Agents for Analysts: How LLMs Are Boosting Productivity](https://www.adamatics.com/blog/ai-agents-for-analysts/): How large language models are changing the day-to-day work of data analysts — and what it means for enterprise analytics teams. - [Internal Analytics Communities: The Quiet Revolution In Digital Transformation](https://www.adamatics.com/blog/internal-analytics-communities/): Discover how internal analytics communities are driving digital transformation by improving collaboration and data culture in organizations. - [Self-Service Data Science: A Trend Here to Stay](https://www.adamatics.com/blog/self-service-data-science/): Why self-service data science is accelerating — and how enterprise teams are enabling analysts to work independently without creating IT bottlenecks or governance gaps. - [Citizen Data Scientists: Hype or the Future of Enterprise Analytics?](https://www.adamatics.com/blog/citizen-data-scientists/): Citizen data scientists help scale analytics by empowering domain experts with modern tools while maintaining governance and quality. - [The New Data Consumer: What Business Analysts Now Look Like](https://www.adamatics.com/blog/new-data-consumer/): Discover how the new data consumer is reshaping analytics in 2025 with AI, modern platforms and better collaboration. - [Democratizing data and analytics: close four gaps, unlock ROI](https://www.adamatics.com/blog/democratizing-data-analytics/): Democratizing data and analytics requires closing four gaps across teams, tools, and platforms. Here's how to unlock ROI at enterprise scale. - [Building a Data Foundation That Lasts](https://www.adamatics.com/blog/building-data-foundation/): Building a data foundation is critical for executives. Discover how to align technology, governance, and business for long-term success. - [Running Generative AI Inside the Firewall: What Enterprise Data Teams Need to Know](https://www.adamatics.com/blog/generative-ai-inside-firewall/): Running GenAI inside the firewall keeps sensitive data on your own infrastructure. A practical look at what enterprise teams need to consider before deploying. - [Beyond Excel: How Analysts Are Empowered by GenAI](https://www.adamatics.com/blog/beyond-excel-genai/): Beyond Excel, analysts are empowered by GenAI to scale insights, automate tasks, and deliver faster business value with secure, governed collaboration. - [Collaborative Analytics: Frontier for Data-Driven Organizations](https://www.adamatics.com/blog/collaborative-analytics/): Collaborative analytics helps data-driven organizations break silos, improve decision-making, and scale insights across teams. - [From Notebook to Enterprise App in Hours](https://www.adamatics.com/blog/notebook-to-enterprise-app/): See how the Adamatics platform helps teams go from notebook to app - fast. Break silos, scale insights, and speed up collaboration securely. - [Turn Ideas Into Impact — Fast](https://www.adamatics.com/blog/turn-ideas-into-impact/): Turn ideas into impact fast. Discover how Adamatics helps teams build, share, and scale secure apps and insights with speed and governance. - [FAIR Data Principles: A Practical Guide for Enterprise Teams](https://www.adamatics.com/blog/fair-principles/): FAIR data means Findable, Accessible, Interoperable, and Reusable. Here's what each principle means in practice and how enterprise data teams implement them without disrupting existing workflows. - [Generative AI Unlocked for Enterprise Data Teams](https://www.adamatics.com/blog/generative-ai-unlocked/): How enterprise data teams are deploying generative AI safely — with full governance, reproducibility, and control over who accesses which models. - [Orchestration & Security](https://www.adamatics.com/blog/orchestration-security/): Discover how Adamatics Orchestration & Security helps teams manage compute, storage, and analytics workflows in one secure platform. Simplify governance, boost performance. - [The Adamatics Workspace – Built for Data Teams](https://www.adamatics.com/blog/adamatics-workspace/): Explore the Adamatics workspace — a shared environment where data scientists, ML engineers, and data engineers collaborate, version, and reproduce their work. - [The Adamatics Integration Layer](https://www.adamatics.com/blog/adamatics-integration-layer/): The Adamatics Integration Layer serves as a centralized API framework within the Adamatics platform, facilitating seamless, secure, and governed access to diverse enterprise data sources. - [The Hidden Costs of a Monolithic Jupyter Setup](https://www.adamatics.com/blog/monolithic-jupyter-setup/): What happens when every data scientist runs their own Jupyter instance — and why the most common data science setup creates reproducibility and collaboration problems. - [European Cloud Providers: What to Know Before You Select One](https://www.adamatics.com/blog/european-cloud-providers/): Explore key factors in choosing European cloud providers - from data sovereignty to compliance and operational strategy. - [Digital Innovation in Research Environments: A Data-First Playbook](https://www.adamatics.com/blog/digital-innovation-research-environments/): Research environments need reproducibility, compliance, and cross-team access. See how modern data platforms are changing the way scientists work with data. - [Building Data-Driven Organizations With the Right Platform](https://www.adamatics.com/blog/building-data-driven-organizations/): What separates data-driven organisations that actually execute from those that only aspire to it — and the infrastructure decisions that make the difference. - [Empowering Innovation: The Potential of the Citizen Data Scientists](https://www.adamatics.com/blog/empowering-innovation-citizen-data-scientists/): Empower citizen data scientists to drive innovation. Learn how self-service tools unlock data potential across your organization. - [Gen AI from a practice point of view](https://www.adamatics.com/blog/gen-ai-practice-point-of-view/): Generative AI is now within reach for mid-sized businesses. With affordable LLMs, RAG systems, and simple setups, AI-driven solutions are easier than ever. This article explores their ROI and how businesses can quickly build scalable AI POCs. - [Digital Autonomy through Containerization](https://www.adamatics.com/blog/digital-autonomy-containerization/): Self-Service Containerization, along with other self-service capabilities, offers a paradigm shift in empowering individuals to deliver digital artefacts autonomously. - [JupyterHub: How to Set It Up and What Problems to Expect](https://www.adamatics.com/blog/jupyterhub-setup-problems/): A monolithic Jupyter setup refers to a basic configuration where JupyterHub and Jupyter Notebooks are run on a single machine—typically a local server—with one or a few kernels. The kernel is the computational engine that executes your code, and in a monolithic setup, all operations (coding, data processing, visualization) are confined to that single machine.