Infrastructure
Compute, storage, and networking supplied by the underlying platform.
Intrisus Synapse
Where controlled infrastructure becomes private intelligence.
A sovereign AI platform direction designed to support GPU compute, training, private inference, RAG, and MLOps.
AI applicationsEnterprise teams
Intrisus SynapseTraining · inference · RAG · MLOps
ModelsDataKubernetesGPUPolicy
Intrisus Open Cloud PlatformCompute · storage · network · operations
Platform layers
AI services built on the same infrastructure foundation.
Synapse is positioned as the AI layer of Intrisus. Each implementation is assembled against validated use cases, models, data, hardware, and governance requirements.
GPU acceleration
GPU architecture designed around validated workload and sharing needs.
Orchestration
Container and workload orchestration based on approved design.
AI services
Training, serving, data, and lifecycle capabilities selected by scope.
Consumption
Governed access patterns for teams and applications.
Potential use cases
A platform direction for enterprise AI teams.
- Private model-serving endpoints
- Controlled model training and fine-tuning
- Retrieval-augmented generation
- Vector data and pipeline architecture
- Model lifecycle and governance patterns
- GPU capacity planning
- Enterprise AI gateway patterns
- Customer-specific AI implementation
Next step
Scope Intrisus Synapse around a validated enterprise AI use case.
Start with your workloads, operating model, and control requirements.