AI Platform as a Service
Sovereign AI PaaS
Your complete AI factory, inside your own boundary.
The full AI lifecycle on infrastructure you own: prepare data, train and fine-tune models, serve inference at production scale, ground answers in your own corpus, and run agents against your real systems — without a single prompt or document leaving your environment.
It is built for organisations that are accountable to a regulator. Every request is attributable, every model version is registered, and the weights learned from your data stay in your registry rather than folding into somebody else’s shared model.
Architecture
How it is put together
Applications and agents
Assistants, copilots, and automated workflows your users actually touch.
Governance and audit
Access control, prompt and response logging, model registry, and approval gates.
Serving and fine-tuning
Inference runtimes and training pipelines that adapt open models to your domain.
Data and retrieval
Ingestion, embedding, vector storage, and retrieval over your own corpus.
Accelerator orchestration
Pooling, fractional sharing, queueing, and scheduling across heterogeneous GPUs.
Capabilities
Model development and training
Notebooks, IDEs, and distributed training on standard open frameworks, executing entirely inside your environment.
Fine-tuning and model serving
Adapt open models to your domain and serve them on modern inference runtimes with continuous batching and cache reuse.
Knowledge base and retrieval
Ingestion, embedding, and vector retrieval over your own documents, honouring the access rules your source systems already enforce.
Agent execution
Run agents against your systems and workflows with full logging, approval gates, and isolated, auditable execution.
GPU sharing and scheduling
Fractional allocation, queueing, and multi-tenant scheduling so research, tuning, and production serving share one pool.
Heterogeneous accelerator support
Treats accelerators from different vendors as one addressable pool, so a supply constraint on one product line does not stop your roadmap.
Model registry and governance
Versioning, promotion, approval, and rollback for every model and agent that reaches production.
Audit-ready by design
Who asked, which model version answered, and which sources it drew on — recorded as a queryable trail, not an investigation.
Where it fits
- Document intelligence and knowledge assistants over sensitive corpora
- Compliance and risk automation inside a regulated boundary
- Predictive models trained on enterprise data that cannot leave
- Turning owned accelerators into a metered internal service
What you end up with
An AI capability your organisation owns outright: the platform, the models, the data, and the people who run it.
The rest of the stack
Infrastructure as a Service
Sovereign IaaS
Virtual machines and containers on one control plane.
Platform as a Service
Sovereign PaaS
The cloud-native operating system for your estate.
Kubernetes as a Service
Sovereign KaaS
Production Kubernetes, operated by us, owned by you.
GPU Platform as a Service
Sovereign GPaaS
Turn the accelerators you already own into a metered service.
High-Performance Computing
Sovereign HPC
Simulation and AI on one cluster, inside your own borders.
Unified Multi-Cloud Management
Multi-Cloud Platform
One control plane across every cloud, regulated data kept in country.
Continuity and Resilience
Disaster Recovery & High Availability
Recovery you have proven, not recovery you have promised.
Cloud to Edge
IoT & Edge Platform
Run every site from one console, even when the link drops.
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Ali Salmaji
DevOps Solution Architect
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