Where applied research meets research infrastructure
Nebula Cloud Labs has expanded.
What began as a research computing offering for universities and institutions now connects two complementary missions on one platform:
Applied Research at Nebula Cloud — investigating emerging technologies, building prototypes, testing new approaches, benchmarking results and translating promising research into capabilities.
Research on Nebula Cloud — providing researchers, universities and R&D organizations with the reproducible computing environments needed to advance their own work.
The core idea is straightforward: the same infrastructure that powers Nebula Cloud's own applied research is available to external researchers. And the insights from external research usage inform how we develop the platform.
Our research areas
Nebula Cloud Labs currently investigates across six domains:
Spatial Intelligence
GeoAI, Earth observation, remote sensing, computer vision, mapping, change detection, feature extraction, point clouds, 3D reconstruction and digital twins. This is where Nebula Cloud's earliest research began — understanding the physical world through computational intelligence.
Engineering Intelligence
AI-assisted CFD, CAE, CAD/BIM, simulation, scientific computing and autonomous engineering workflows. The question: can AI reason about engineering problems the way an experienced engineer does — selecting tools, configuring simulations, interpreting results and iterating toward solutions?
Agentic Systems
Autonomous execution, capability routing, tool orchestration, computer use, verification, recovery, supervisors and multi-agent coordination. This research directly informs Nebula Cloud Studio's agent runtime — how autonomous systems plan, execute and verify complex multi-step workflows.
Physical AI & Autonomy
Robotics, autonomous vehicles, simulation environments, ROS 2, CARLA, Autoware and embodied intelligence. Exploring how AI systems can operate in and reason about the physical world.
AI Systems
Efficient inference, constrained-resource execution, local models, cloud/local/hybrid strategies, AI appliances and model-runtime optimization. Not every workload needs a data center — how do we make AI practical on limited hardware?
Document Intelligence
Multimodal understanding, OCR, retrieval, evidence extraction, knowledge graphs, document reasoning and autonomous document workflows. Turning unstructured documents into structured, queryable, actionable knowledge.
The research model
Our approach follows a deliberate progression:
Research — Investigate a problem space. Understand what is known, what is possible, what is missing.
Experiment — Build prototypes. Test approaches. Measure outcomes.
Validate — Benchmark against alternatives. Verify claims with real data. Document methodology.
Demonstrate — Create working demonstrations that prove capability.
Operationalize — When research matures, translate it into production capabilities within Nebula Cloud Studio, Universal Workbench, Capability Packs or the broader platform.
Not every experiment becomes a product. Research that does not produce expected results is still valuable — it informs what we build and how we build it.
Research infrastructure for everyone
The second mission remains as important as ever.
Universities, research institutes, faculty, PhD researchers, student labs and enterprise R&D teams need computing environments that are:
- Reproducible — same environment, same results, every time
- Accessible — GPU compute without months of procurement
- Domain-ready — preconfigured for AI/ML, geospatial, engineering, simulation, HPC
- Governed — institutional access control, resource allocation, usage tracking
- Scalable — from a single notebook to multi-node HPC
Nebula Cloud Labs provides all of this through the same platform infrastructure our own research uses.
What this means
For researchers: access to the same computing environments, workbenches and AI infrastructure that Nebula Cloud uses internally — without building it from scratch.
For the platform: research findings inform real capability development. Every experiment, successful or not, improves what we can offer.
For the industry: transparent research practice. We share methodologies, benchmarks and findings rather than making unsubstantiated claims.
Explore
Visit Nebula Cloud Labs to see our current research areas, featured projects, available research environments and collaboration opportunities.
If you're working on problems that intersect with our research areas — spatial intelligence, engineering, agentic systems, physical AI, document intelligence or efficient AI — we'd welcome a conversation about collaboration.
Related: Nebula Cloud Labs · Research Computing · Workbenches · Academia