Presenter: Kai-Michael Kammer from EAWAG
NTSuisse is a national Swiss water-quality monitoring platform for high-resolution mass spectrometry (HRMS) data, used by cantonal environmental agencies, water suppliers, and the Swiss Federal Office for the Environment. The platform centralises data upload, automated processing, target and suspect screening, semi-quantification, and visualisation. It runs on Azure (with a separate staging instance), while the architecture supports local deployment equally well, which is how the development team iterates day-to-day.
The platform is built and maintained by a team of three which is atypically large for a science project, but still small by any software-engineering standard. The stack is polyglot: React + TypeScript frontend, Python/Airflow orchestration, PostgreSQL + LabKey for data and auth, and Docker throughout. The backend enforces a strict layered architecture, separating pure functional logic separated from I/O, which keeps the offline test suite fast and refactoring safe. CI enforces linting, formatting, and the offline test suite at every merge request.
We also share our experience integrating AI tooling beyond code completion: project-specific instruction files, domain-knowledge skill files, and agent-callable planning workflows that give the LLM enough context to reason about architecture, not just syntax.
What you will take away:
- practical patterns for structuring a science platform which is being actively used by many partners
- a concrete approach to making AI assistants useful at the project level
Audience: RSEs comfortable with Python and web stacks; no mass-spec background needed.