R&D and Labs
GPUaaS for simulation and training, notebook workspaces, data integration pipelines, and ZTNA for collaborative R&D environments.
Why R&D and Labs chooses Clevertek
Research and development output depends on cycles of compute, data movement, and collaboration — and the infrastructure investment is only justified if it accelerates rather than impedes the science. A modelling sprint or simulation run requires GPU compute now, not after a procurement cycle that takes longer than the experiment timeline. A shared research dataset must be findable, governed, and reproducible rather than copied into ten unsecured notebook environments with version drift. A result that cannot be reproduced due to missing data provenance or environment configuration drift is not a valid result under scientific method. Clevertek gives R&D and lab teams a research-grade foundation: GPU-as-a-service and multi-GPU training clusters for simulation and model training without capital hardware commitment, notebook workspaces and an AI Studio platform with pre-installed ML frameworks and GPU scheduling so scientists experiment without provisioning and maintaining their own infrastructure, data integration and governance with ETL/ELT pipelines, lineage tracking, and quality controls so datasets are connected, auditable, and reproducible across the team, and zero-trust access with managed security so collaborative partner-shared research environments are contained to the specific resource rather than exposing the full lab network. Sovereign cloud infrastructure keeps sensitive or regulated research data within the required jurisdiction. The laboratory directs its energy toward discovery and experimental iteration rather than resolving cluster configuration issues.
How Clevertek helps R&D and Labs on its journey
Research output depends on compute cycles, data movement, and collaboration — and infrastructure is only valuable if it accelerates the science rather than impeding it. A modelling sprint needs GPU now, not after procurement; a shared dataset must be governed rather than scattered across unsecured notebook environments; an irreproducible result is not a result.
We give R&D and lab teams a research-grade foundation: GPU-as-a-service and multi-GPU training clusters for simulation without capital commitment, notebook workspaces and an AI Studio with pre-installed frameworks and GPU scheduling so scientists work without provisioning infrastructure, and data integration with lineage tracking and quality controls so datasets are findable, auditable, and reproducible.
Zero-trust access and managed security provide partner collaboration that is contained to the specific resource. Sovereign cloud keeps sensitive research data in-country. The laboratory invests its energy in discovery and iteration rather than in cluster configuration and environment drift remediation.
Solutions that serve R&D and Labs
Each product is delivered as a managed service and scoped to your environment.
GPU as a Service
Learn moreTraining Clusters
Learn moreNotebook Workspaces
Learn moreAI Studio & Data Platform
Learn moreAI Inference Endpoints
Learn moreData Integration & Governance
Learn moreData Visualization
Learn moreZero Trust Network Access
Learn moreManaged Detection & Response
Learn moreSovereign AI & GPU Cloud
Learn moreFrequently asked questions
Can we get GPU for a sprint without buying hardware?
Yes. GPU-as-a-service and multi-GPU training clusters are available on demand — per-hour for flexible experimentation or reserved for production workloads. A simulation or model training run consumes capacity during the work window and releases it on completion, with no hardware procurement cycle or idle-utilisation risk between experiments.
How do we keep research datasets connected and auditable?
Data integration and governance provides ETL/ELT pipelines, automated lineage tracking, and data quality controls. Datasets remain findable, trusted, and reproducible across the research team rather than drifting into disconnected, ungoverned copies across individual notebook environments.
Can researchers experiment without standing up their own stack?
Yes. Notebook workspaces and the AI Studio platform provide pre-built, GPU-backed environments with established ML frameworks (PyTorch, TensorFlow, CUDA toolkit) installed. Researchers progress from hypothesis to experiment without assuming the responsibilities of platform engineering.
How do we collaborate with partners without exposing the whole estate?
Zero-trust access with identity-aware proxying and managed detection ensures that shared or partner research environments are contained to the specific authorised resource. Collaboration does not create a broad network-level access path to the rest of the lab's infrastructure.
Can we keep sensitive or sovereign research data in-country?
Yes. Sovereign AI and GPU cloud infrastructure runs workloads in India-located zones with customer-managed encryption keys. Regulated or strategic research data and the models trained on it remain within the jurisdiction for which you are accountable.
Build your R&D and Labs roadmap
Tell us your outcome — we will map the right products and fabrics to get you there.