Services
AI Infrastructure & High-Performance LLMOps
Run large language models with the latency, throughput, cost control and governance of any other tier-1 platform - in your cloud, your data center or a fully air-gapped enclave.
Overview
Getting a large language model to answer a question in a demo is easy. Running it reliably for thousands of concurrent users, at a predictable cost per token, with full audit trails and without silent performance regressions, is an infrastructure problem, and that is where most AI programs quietly fail. Cystem X engineers the full stack beneath your AI applications: GPU cluster design and capacity planning, model serving and inference optimization, and the retrieval-augmented generation and knowledge platforms that ground model outputs in your own data. For regulated and sovereign environments we design and operate air-gapped and private deployments with the same reliability discipline as public cloud, including the update and patching process for a disconnected enclave. Observability and evaluation are treated as first-class infrastructure, not an afterthought, so you can see latency, cost per token and output quality before your customers or regulators find a problem first. The result is an AI platform your operations team can run with the same confidence as any other tier-1 production system.
GPU cluster engineering
Model serving
RAG & knowledge platforms
Sovereign & air-gapped AI
Capabilities
Core capability areas we deliver within this pillar.
Deployment options
The same platform engineering discipline, applied to the environment your data residency and regulatory requirements call for.
Deployed inside your existing AWS, Azure, Google Cloud or Oracle Cloud account, using managed GPU instances and auto-scaling to match demand. This is usually the fastest path to production and the easiest to integrate with cloud-native data and identity services you already run.
Run entirely on infrastructure you own, in your own data center. We size and provision the GPU cluster, build the serving and observability layers, and hand over (or continue to operate) a platform with no dependency on a public cloud account, suited to strict data residency requirements.
A fully disconnected deployment with no outbound internet access, including a defined, auditable process for applying model, security and dependency updates inside the air gap. This is the model most government, defense and critical-infrastructure engagements require.
How we deliver
A consistent four-stage delivery path, from first assessment to ongoing operation.
Assess
We size workloads against your latency, throughput and cost targets, and determine whether cloud, on-premises or air-gapped deployment fits best.
Design
Architects design the serving layer, GPU topology, retrieval pipeline and observability stack before any infrastructure is provisioned.
Build
Engineers stand up the platform, optimize inference and validate it against defined latency and cost-per-token service levels.
Operate
We run and continuously tune the platform in production, including the update process for disconnected environments.
Outcomes
What changes for your organization once this pillar is in place.
Lower cost per token
Inference optimization and right-sized infrastructure reduce the unit economics of running models in production.
Predictable latency
Capacity planning and serving-layer tuning keep response times inside agreed service levels under real load.
Reproducible releases
Model and configuration versions are tracked, so every release can be reproduced, rolled back or audited.
Full audit trail
Every inference and retrieval call is logged, satisfying governance requirements from day one.
Technology
A representative slice of the platforms and tools we use to deliver this pillar.
Frameworks & standards alignment
Our delivery practices are aligned with these recognized frameworks and standards.
Engagement models
Choose the model that matches where you are today, with no fixed pricing until scope is agreed.
Assessment
Assessment
2 to 4 weeks. A focused review that produces clear findings and a prioritized roadmap.
- Current-state review
- Prioritized findings
- Actionable roadmap
Project
Project
Fixed scope, with defined deliverables and milestones agreed before work begins.
- Defined scope & deliverables
- Milestone-based delivery
- Architect-led team
Managed / Dedicated
Managed / Dedicated
Monthly engagement for ongoing operation, or a dedicated team embedded with yours.
- Ongoing operation
- Transparent governance
- Scales with demand
Related industries & insights
Where this pillar shows up most, and further reading from our engineering team.
Industry
Banking & Financial Services
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Healthcare & Life Sciences
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Telecommunications & Media
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ExploreArticle
Running LLMs on-premises: a reference architecture for regulated organizations
Hardware sizing, serving layer, gateway, RAG, observability and the air-gap update process.
Read article