From GPU infrastructure to production AI.
HBS designs and delivers the infrastructure, platforms, and operational systems required to run AI reliably in production.
We work with enterprises, infrastructure providers, and regulated organizations that need control over their compute, data, security, and operations.
GPU Infrastructure · Production Inference · AI Development Environments · AI Last-Mile EngineeringCompute is only the beginning.
GPUs, models, and data do not become a production AI capability on their own. They require the right networking, storage, orchestration, model-serving layer, security controls, observability, and operating model.
HBS brings these elements together into coherent systems that engineering teams can operate, scale, and trust.
Four ways we build AI capability.
Complete GPU environments for training, inference, and shared AI workloads
Infrastructure architecture, high-performance networking and storage, workload orchestration, GPU allocation, tenant isolation, observability, security, performance validation, and operational handover. The outcome is not simply installed hardware — it is an operational AI infrastructure platform.
From experiments to secure, observable, scalable production services
Model serving, OpenAI-compatible endpoints, GPU scheduling, autoscaling, AI gateways, access control, usage metering, performance optimization, and private or hybrid deployment. We focus on the operational path between a working model and a dependable production service.
Closing the gap between available infrastructure and usable AI capability
For organizations that already have compute, models, data, or AI initiatives: private AI platform architecture, model and data integration, enterprise AI gateways, retrieval systems, multi-tenant services, deployment workflows, security controls, and operational readiness.
Isolated environments to develop, evaluate, and deploy AI systems
Controlled access to GPUs, models, data, and development tools — with resource quotas, workload isolation, identity integration, auditability, and support for restricted or air-gapped operation. Teams build quickly while infrastructure owners retain control.
Independent assessment, target architecture, technical due diligence, capacity planning, implementation roadmaps, and operating-model design.
Identity and access architecture, workload isolation, policy enforcement, supply-chain controls, vulnerability management, audit evidence, secure disconnected operations.
Operational visibility across infrastructure, GPUs, clusters, services, and models — health, capacity, utilization, logs, metrics, traces, diagnostics, recovery.
Ongoing lifecycle management, upgrades, security response, reliability support, capacity planning, performance optimization, continuous improvement.
A disciplined path from architecture to production.
We examine the infrastructure, workloads, constraints, risks, team capabilities, and operating model.
We define the target platform, security boundaries, supported configurations, implementation sequence, ownership, and acceptance criteria.
We test critical assumptions against real infrastructure and representative workloads through a bounded pilot.
We integrate and validate the infrastructure and software as one operational system.
We automate recurring procedures, document the platform, test failure scenarios, and prepare the responsible team.
We use production evidence to improve reliability, security, performance, utilization, and infrastructure economics.
Architecture Assessment
A focused review of infrastructure, workloads, platform readiness, security requirements, operational risks, and investment priorities.
Production Pilot
A bounded implementation that validates the architecture against real workloads and measurable acceptance criteria.
Platform Delivery
End-to-end implementation, validation, documentation, and operational handover of an agreed AI infrastructure or platform scope.
Ongoing Operations
Long-term platform support, release management, security response, reliability monitoring, and continuous improvement.
Organizations building long-term AI capability.
We are most useful when the challenge is not simply installing software, but creating a platform that must remain reliable after the initial project ends.
Built for environments where control matters.
We work across compute, network, storage, Kubernetes, security, AI runtime, and operations rather than optimizing one isolated layer.
We prefer open technologies, portable systems, and clear ownership of infrastructure, data, and operations.
We select technology according to the workload and operating model — not according to a reseller catalogue.
Installation is not the end of the project. A platform must be supportable, observable, upgradeable, and recoverable.
We are explicit about technical limitations, delivery risk, ownership, and what remains unproven.
Architecture before procurement. Technology decisions should follow workloads, constraints, and the operating model.
Integrate, do not reinvent. We use proven technologies and write new software only where it creates necessary operational value.
Evidence before claims. Security, reliability, and performance should be tested and measurable.
Automation before heroics. A production system should not depend on one engineer's memory.
Capability before dependency. Our work should leave the client with a stronger platform and a stronger operating team.
About HBS
HBS is an AI infrastructure and platform engineering company based in Astana, Kazakhstan, working with clients and partners internationally.
We focus on the engineering required to move from hardware and experimentation to dependable production AI. Our team combines experience in private cloud, large-scale GPU infrastructure, Kubernetes, model serving, cybersecurity, regulated environments, and open-source engineering.
We are building HBS as a focused engineering company: technically independent, operationally accountable, and selective about the work we accept.
Make advanced AI infrastructure operable, supportable, and accessible to organizations that need control over their technology and data.
HBS builds on open source first and designs for minimal vendor lock-in. We are pragmatic, not dogmatic: we integrate proprietary software where it serves the workload. We take responsibility for architecture, integration, validation, security, lifecycle management, and support. Our value is not a longer list of tools — it is the complete operational system built around them.
Our engineers come from the core team behind QOSI and were directly involved in delivering a national-scale GPU cluster in Kazakhstan. The team includes CNCF Kubestronauts and upstream contributors to OpenStack and other open infrastructure projects.
That experience is the point: it shortens the path to production and avoids the expensive first-time mistakes of building GPU infrastructure.
Qasym Majen · LinkedIn
Build with HBS.
We collaborate with data centers, infrastructure vendors, cloud providers, open-source communities, security specialists, research organizations, and specialist engineering firms. We are interested in partnerships with real customer demand, clear technical ownership, and a shared commitment to reliable delivery.
Partner with HBSWork on infrastructure for real AI workloads.
We are building a focused team across GPU infrastructure, Kubernetes and platform engineering, distributed systems, model serving, security, observability, and reliability engineering. We value people who think across system boundaries, communicate directly, document their work, contribute upstream, and take responsibility for systems in production.
Building serious AI infrastructure?
Tell us what you are trying to run, what infrastructure you already have, what constraints matter, and where the current architecture stops working. We will tell you directly whether HBS is the right partner.
Discuss a Project
Give us enough context to understand the infrastructure, workload, and current constraint. Technical detail is welcome.
Thank you. We will review the technical and commercial context and respond if there is a clear fit.