Marshal Code
AI-assisted software engineering.
Repository analysis, implementation, testing, and verification under developer supervision and review.
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Backed by engineers from
Supported by startup programs
Claude for Startups
Z.ai for Startups
Neo4j Startup Program
The compute stack for superintelligence.
We build AI compute and infrastructure that makes every GPU work harder. SCL optimizes the full AI compute stack — from GPUs, networking, and storage to runtimes, inference, orchestration, and workloads.
Higher performance. Better utilization. Lower cost.
Products & capabilities
Four capabilities, one coherent system.
Marshal Code, private infrastructure, governed agents, and evaluation fit together — each useful alone, stronger combined.
Private AI infrastructure
Model serving inside your boundary.
Serve and orchestrate models on cloud, private cloud, or on-premise infrastructure you control.
Explore SCL CloudGoverned agents
Automation with approval points.
Permissions, tool-access boundaries, human review, and full audit trails on every run.
See enterprise controlsEvaluation & reliability
Testing before trust.
Benchmarks, regression tracking, and promotion gates so models earn production use.
See researchAI is scaling. Compute is the bottleneck.
More capable AI requires more compute. Simply adding GPUs isn't enough.
Idle accelerators, inefficient workloads, memory bottlenecks, network overhead, and fragmented infrastructure leave performance and capital on the table.
SCL turns infrastructure into optimized, usable compute.
Optimize the entire compute stack.
Compute
GPU and CPU infrastructure built for training, inference, agents, and AI workloads.
Optimization
Maximize GPU utilization, memory efficiency, throughput, and cost efficiency.
Runtime
High-performance runtimes for training, inference, and model serving.
Orchestration
Intelligent scheduling and workload placement across heterogeneous compute.
Networking
High-performance infrastructure for distributed AI workloads.
Infrastructure
Deploy across public cloud, private cloud, on-premise, and sovereign environments.
More intelligence from every GPU.
Higher utilization
Get more productive work from the infrastructure you already have.
Lower cost
Reduce idle capacity and cost per AI workload.
Higher throughput
Run more training and inference on the same compute.
Faster scaling
Move from a single GPU to distributed infrastructure without rebuilding your stack.
Industries
Deployed where constraints are real.
Each sector gets its own engagement scope — distinct data boundaries, review requirements, and validation criteria.
Defense & National Security
Controlled models, data, and execution.
Private AI deployment, governed agents, and evaluation for mission-critical engineering.
ExploreFederal & Public Sector
Modernize document-heavy workflows.
Processing, integration, and governed automation with human review built in.
ExploreFinancial Services
Private inference with evidence.
Evaluation, controlled automation, and replayable audit trails for financial teams.
ExploreHealthcare & Life Sciences
Authorized data, professional oversight.
Pipelines, research infrastructure, and evaluation with professionals in the loop.
ExploreTechnology
Engineering leverage that ships.
Marshal-assisted development your platform team governs across repositories.
ExploreTelecommunications
Support for network operations.
Incident-analysis assistance, edge inference architecture, and software maintenance.
ExploreBuilt for the AI workloads of tomorrow.
From experimentation to production, SCL provides the compute and infrastructure layer needed to run increasingly capable AI.
TrainingPost-trainingInferenceAgentsEvaluationSimulation
One stack. Any infrastructure.
Run AI where your data and workloads need to live — while managing compute through a unified infrastructure layer.
CloudPrivate CloudOn-PremiseSovereign
Deployment & engagement
Run where your data lives.
Cloud, private cloud, or on-premise — scoped per engagement with governance controls and validation gates. Most engagements follow a simple path: assess, architect, build, validate, integrate.
Build what comes next.
The next generation of AI needs a new compute stack.
SCL is building it.