XePlatform: Managed, Private and Hybrid AI Operations

AI experiments show promise. Operations determine success.

Getting AI models to work is increasingly commoditised. The operational layer around them - security, governance, observability, release management - is not. Most enterprise AI teams are forced to choose between two options that both fail them:

• Send data to a vendor's infrastructure and move fast, but lose sovereignty, face token costs at production scale, and accept vendor lock-in.
• Keep data sovereign and build everything yourself, but spend 6 to 18 months, hire platform engineers, and own every incident at 2am. XePlatform removes that trade-off entirely.

Three deployment modes. One operational platform.

Managed AI - You use Bedrock, Vertex, or Azure AI. Model access is solved. But as you add agents and pipelines, production becomes unmanageable. XePlatform operates the full Kubernetes and agent orchestration layer inside your AWS account. Release pipelines and observability around your existing endpoints. No provider change needed.

Private AI - You want to run your own models on GPU without calling any external API. Full sovereignty. No token billing. XePlatform operates the full private GPU stack: Kubernetes, LLMOps, security, and observability. Release engineering built in. Staging drift eliminated by architecture.

Hybrid AI - You need both simultaneously. Private AI for proprietary assets. Managed AI for commodity operations. One platform. Inside your AWS account.

What XePlatform operates inside your AWS account XePlatform operates on a strict split-plane model. The control plane manages platform orchestration only. It has no access to your data, secrets, models, or application infrastructure. Everything that matters lives in your execution plane - inside your cloud account, your IAM, billed to your account:

• Private LLM Models and GPU Nodes
• Agent Orchestration and Auto-scaling
• Observability and Audit Trails
• Automated Backups and Rollbacks
• Release Pipelines and Security Enforcement

Golden path release engineering Most platforms monitor failures after deployment. XePlatform prevents an entire class of failures by eliminating staging-to-production drift before release. Golden path release engineering ensures every AI workload follows a proven, standardised deployment process from staging to production. 75% lower MTTR compared to manual release workflows.

Getting started: your path to production

1. Subscribe through AWS Marketplace and schedule a discovery call.
2. XePlatform deploys the control plane and configures your execution plane inside your AWS account.
3. Connect your existing AI endpoints (Bedrock, SageMaker, or private models) or deploy new ones.
4. Release pipelines, observability, and security enforcement activate automatically.
5. Your AI workloads are production-ready - typically within hours of onboarding. Prerequisites: An active AWS account with appropriate service quotas for your workload (GPU instances if running Private AI). XePlatform handles all Kubernetes, networking, and platform configuration.

Built for regulated industries Financial Services. Pharma. Legal. Healthcare. Manufacturing. Industries where data sovereignty is non-negotiable and compliance is mandatory. All logs, traces, audit trails, and execution records are stored inside your own cloud account from day one, accessible to your team without involving XePlatform. Structurally compliant with GDPR, EU AI Act, DORA, NIS2, and MiFID II - by architecture, not by contract.

Ready to evaluate? All compute and storage billed directly to your AWS account. XePlatform's control plane handles orchestration only - no credentials, no IAM role, no network path into your data. Contact us to schedule a discovery call