AI-ready cloud services for scalable R&D infrastructure
Design cloud or hybrid environments around real scientific workloads, with the compute, storage, orchestration and cost controls your teams need.
Ardigen provides AI-ready cloud services for omics, imaging and AI workloads that have outgrown ad hoc infrastructure decisions. We assess how teams use the current environment, identify performance and cost bottlenecks, then design and implement a cloud or hybrid setup that fits the workload and operating model.
Where cloud and on-premise environments limit R&D
Most research teams already use a mix of on-premise infrastructure and cloud services. The constraint differs by environment. Local systems can limit access to managed services or elastic capacity. Cloud platforms offer those capabilities, but poorly matched compute, storage and scheduling choices can make cost the dominant concern.
The effects are practical. Genomics jobs wait for resources. Image-processing runs exhaust memory. Teams move large datasets more often than the analysis requires. GPU instances stay idle between jobs, while storage and data-transfer charges grow without a clear owner.
Repeatability also depends on more than containers. Teams need versioned environments and pipelines, plus captured parameters, metadata, metrics and monitoring. Without that operating layer, the same workflow can produce avoidable differences across users, projects or time points.
Cloud architecture designed around life sciences workloads
AWS, Azure and Google Cloud already provide the scale required for demanding research. The design task is choosing and configuring the right services for the data shape, access pattern, runtime, team skills and budget. A whole-genome pipeline, a high-content imaging workload and distributed model training place different demands on storage, memory, networking and scheduling.
Ardigen starts with those workload profiles. The target design may retain on-premise systems, use cloud capacity selectively or move specific workflows to managed services. Provider and tool choices follow the requirements rather than a fixed stack.
For discovery R&D, the default priorities are performance, repeatability, observability and cost control. Where clinical or other sensitive data enters scope, the architecture also defines the relevant identity, access, encryption, residency and review controls. It does not claim that infrastructure alone creates compliance.
What we help you do
Scale omics and imaging workloads
Match memory, storage and scheduling to genomics, multiomics and high-content imaging pipelines rather than adding undifferentiated capacity.
Control cloud spend
Right-size compute, use storage tiers deliberately, schedule resources around demand and give teams visibility into the cost of jobs and programs.
Use cloud and on-premise resources together
Design data movement, access and workload placement for a hybrid environment when a full migration is unnecessary or uneconomic.
Make execution repeatable
Version workflows and environments, then track parameters, metadata and metrics needed to understand how a result was produced.
Operationalize GPU and distributed workloads
Configure capacity, scheduling and monitoring for model training or other accelerated workloads, subject to the selected use case.
Protect sensitive workloads where required
Translate the applicable privacy, security and review requirements into identity, access, encryption and logging controls.
A four-part path to scalable research infrastructure
Workload and cost assessment
Map compute, storage, transfer and access patterns. Identify queue, memory, throughput and spend bottlenecks, including resources that are over-provisioned or poorly scheduled.
Cloud or hybrid architecture design
Define how data and jobs should move across local and cloud components. Select storage tiers, compute patterns, networking and scaling rules around the actual R&D workload.
Workflow execution and repeatability
Implement fit-for-purpose orchestration and versioned environments. Capture pipeline versions, hyperparameters, metadata and metrics so teams can inspect and repeat runs.
Operations and observability
Set monitoring, alerts, cost allocation, access responsibilities and handover documentation so the environment remains usable after launch.
Request an infrastructure assessment.
Concrete, reusable deliverables
Infrastructure assessment report. Workload inventory, bottleneck analysis, cost drivers and risks linked to priority programs.
Target-state architecture. Cloud or hybrid blueprint covering compute, storage, networking, workflow execution, access and operations.
Execution design. Recommended orchestration, environment versioning, parameter and metadata capture, metrics and monitoring.
Cost and operating controls. Resource policies, ownership, tagging or allocation logic, alerts and review cadence appropriate to the engagement.
Implementation and handover. Deployment scope, technical documentation and agreed responsibilities for the internal team and Ardigen.
Outcomes the infrastructure
is designed to support
Workload-aligned performance
Teams can run priority pipelines on resources selected for their real memory, storage and scheduling needs.
More visible cloud economics
Owners can connect spend to workloads and act on idle, over-provisioned or poorly placed resources.
Repeatable execution and results
Versions, parameters, metadata and metrics make it easier to inspect and reproduce computational runs.
A maintainable operating model
Monitoring, access responsibilities and documentation are defined before the environment is handed over.
Frequently Asked Questions
We already use cloud. Where would an architecture review help?
Many teams start with project-level cloud setups that work well at first, but become harder to manage as data volume, users, workflows, and GPU needs grow. An architecture review identifies where cost, storage, access, orchestration, and reproducibility may become constraints, then defines what needs to change before active programs are affected.
What makes life sciences R&D infrastructure different?
The difference is not only scale. Life sciences workloads combine large raw files, scientific metadata, sensitive datasets, specialized workflow engines, GPU-intensive AI tasks, and reproducibility requirements. The infrastructure needs to support how scientists and data teams actually work: from raw omics or imaging data through processing, tracking, analysis, and model-ready outputs.
Can you support hybrid infrastructure across on-premises and cloud?
Yes. Many R&D teams need to keep part of their compute or storage on-premises while using cloud services for elastic compute, GPU workloads, storage tiers, or collaboration. We design hybrid architectures that connect both environments with consistent access, data movement, workflow execution, and cost control.
How do you handle sensitive biomedical data?
We design access controls, encryption, identity management, lineage, and data residency considerations into the infrastructure where sensitive or patient-linked data is involved. Privacy and compliance requirements are addressed based on the data type, geography, and client policies.
Which workflow types can the infrastructure support?
We support bioinformatics workflows for raw genomics, transcriptomics, and multiomics data using frameworks such as Nextflow or Snakemake. For structured data processing, orchestration, and analytics environments, the architecture may include tools such as Airflow, Databricks, or cloud-native services. The stack depends on workload type, existing team practices, and operating requirements.
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