Make AI useful to the research.
Develop document and data pipelines, research assistants, model evaluation workflows, and tools that fit your team's methods.
AI for research →HPC · Scientific computing · Research AI
Move the research.
Not the bottleneck.
Build AI solutions. Fix slow systems. Give researchers more time for the work that matters.
From a lab's first AI workflow to a shared GPU cluster: we connect models, software, infrastructure, and the research process—and measure whether the change helps.
For research teams, computing cores, and scientific R&D.
Universities & research institutesLife sciences & biomedical researchNational labs & industrial R&D
One practice. Three entry points.
New ideas. Stalled jobs.
Systems with more to give.
Find what is slowing the science.
Diagnose failed jobs, idle GPUs, slow filesystems, data stalls, and unreliable environments. Establish the cause before changing the system.
HPC troubleshooting →Get more from the capacity you have.
Optimize scheduling, inference, data movement, and research processes. Compare useful throughput, output quality, and cost before and after.
Performance engineering →From the model to the machine
An AI factory needs
more than GPUs.
Model choice, batch size, memory, scheduling, storage, and the fabric all affect how much useful work a cluster delivers. We trace the workload across those boundaries.
Explore HPC & GPU services →MODELS
Choose against the actual research task.
Hosted or open-weight models, representative evaluations, retrieval, fine-tuning, and quality/cost tradeoffs.
COMPUTE
Maximize useful GPU utilization.
Profile idle time, memory pressure, concurrency, batching, job placement, and inference latency.
DATA & FABRIC
Keep the workload fed.
Parallel storage, metadata, data staging, InfiniBand/RoCE, and distributed job performance.
Research environments
Built around how your team works.
We work with your scientific leads and computing staff. Domain decisions and scientific validation stay with the research team.
Universities & shared cores
Fair-share scheduling, onboarding, reproducible environments, shared GPU access, and usage visibility.
Life sciences & biomedical teams
Analysis pipelines, research data workflows, document extraction, and controlled AI evaluation using approved data.
Physics, materials & engineering
Simulation workflows, distributed workloads, scientific software environments, and model-assisted analysis.
Research institutes & industrial R&D
Private knowledge tools, AI prototypes, capacity planning, and moving a useful experiment into a maintained system.
Start with a defined piece of work
A practical first step.
A clear price.
Book a consultation or diagnostic. For a build, optimization sprint, or ongoing support, we agree scope and payment milestones before you commit.
Full scopes and pricing →Research computing consultation$450HPC / AI troubleshooting$1,800HPC performance assessment$4,500Research AI model evaluation$3,500
USD · Professional service fees · Card, bank transfer, or an agreed institutional invoice.
Bring the question or the bottleneck
What should your research
computing do better?
An idea to build, an issue to solve, or a workflow to improve is enough to start.
Talk through the project ↗