Research Infrastructure
Research infrastructure that stands behind the paper
We build AI infrastructure for academic research groups — HPC clusters, training pipelines, reproducibility environments, and tooling for code sharing with industry sponsors. Our approach: the infrastructure should disappear from the researcher's path, not become the project itself.
What this covers
- HPC clusters with schedulers, monitoring, and fair sharing
- Reproducibility-first — every experiment captured with its full environment
- Bridges to industry research partners for IP transfer
- Ongoing support through the academic calendar, not just at setup
What's inside the sector
- HPC clusters with full GPU support (A100, H100, peers)
- Schedulers, monitoring, fair sharing across teams
- Training pipelines with full per-experiment versioning
- Virtual environments for reproducibility (containers, MLflow)
- Bridges to industry sponsors — clean IP transfer
- Ongoing support through the academic calendar, not just setup
- Scientific computing — simulation, complex system solving
Who it's for
University lab heads, grant-funded research groups, industry research institutes that partner with academia.
Selected work
- Aquatis — A water-pump anomaly-detection model trained on infrastructure we built for their research team. Infrastructure with a clear path from research startup to industrial production.