Common questions about the Adamatics platform — what it does, how it’s deployed, how it integrates with your existing stack, and how it’s priced. If you need a deeper conversation, book a call.
Adamatics is a collaborative data science platform for enterprise teams. It provides shared, governed workspaces where data scientists, ML engineers, and data engineers work together in the same environment — versioning notebooks, reproducing analyses, and deploying models and apps directly from their existing tools. The platform runs inside your own infrastructure and is designed as a managed alternative to fragmented local Jupyter setups.
A standard JupyterHub setup gives individual users isolated notebook environments but does little to support collaboration, reproducibility, or governance at scale. Adamatics adds shared workspaces, version control, access management, app deployment, and a reusable integration layer on top — so teams can move from individual analysis to shared, production-ready outputs without rebuilding their stack.
Yes. The platform is built around the FAIR principles — Findable, Accessible, Interoperable, and Reusable. Centralised access controls, consistent metadata standards, open file formats, and full data lineage tracking mean FAIR compliance is built into the workflow rather than applied retrospectively. This is particularly relevant for teams in pharma, financial services, and publicly funded research where FAIR compliance is tied to regulatory or funding requirements.
Adamatics is used primarily by enterprise teams in pharmaceutical research, financial services, and research institutions — industries where reproducibility, auditability, and data governance are operational requirements. The platform is also used by technology companies scaling self-service analytics across large, distributed data teams.
Adamatics deploys on your preferred cloud provider — AWS, Azure, GCP, or private cloud — using Kubernetes-based containerised environments. It runs within your own infrastructure, meaning data never leaves your environment. IT retains full control over firewalls, access management, and governance.
No. The platform is built entirely on open-source components and open file formats. You retain full ownership of your code, container images, and data. If you choose to migrate, there are no proprietary barriers — any experienced data engineer can operate the underlying infrastructure independently.
Yes. The platform is designed to support generative AI and large language model workflows within your own infrastructure. This means sensitive data never leaves your environment — a common requirement for enterprises in regulated industries or with strict data residency policies.
Yes. Data scientists and analysts can deploy apps and dashboards directly from their notebooks through the Adamatics platform. IT sets the governance boundaries upfront; business users operate within them independently. This removes the bottleneck of central IT involvement for every deployment without compromising security or access controls.
Adamatics connects to databases and data lakes via role-based access control through your identity provider. It includes pre-built connectors and supports standard protocols including SQL. Custom integrations with systems such as SAP and other enterprise services are also supported.
Adamatics is designed for seamless integration with your existing data infrastructure. It connects to databases and data lakes via role-based access control through your IDP. Besides pre built connectors, and support for standard protocols like SQL Adalab enables custom integration to systems like SAP, and custom services.