About Stratosync

Specialists in AI cloud infrastructure and platform engineering

Stratosync was established in 2019 by a team of cloud architects and machine learning engineers who recognized a growing need for specialized infrastructure expertise as organizations began deploying AI workloads at scale. Our founding team had worked on large-scale distributed systems at technology companies and saw firsthand the complexity involved in building platforms capable of supporting computationally intensive model training and deployment.

Based in Cyberjaya, Malaysia's technology hub, we serve clients across Southeast Asia and beyond. Our location positions us at the intersection of established technology sectors and emerging AI innovation, allowing us to understand both the technical requirements and business contexts that drive AI infrastructure decisions in the region.

Our mission centers on making AI infrastructure accessible and manageable for organizations at different stages of their AI journey. Whether a company is conducting its first infrastructure assessment or scaling an existing ML platform to support dozens of data scientists, we provide the technical depth and practical implementation experience needed to build reliable systems.

We work primarily with CTOs, infrastructure teams, and data science leaders who need to translate AI ambitions into functioning technical platforms. Our engagements focus on assessing compute requirements, designing scalable architectures, implementing MLOps pipelines, and establishing operational practices that allow teams to work efficiently with AI workloads.

The infrastructure landscape for AI continues to evolve rapidly. New compute options, orchestration tools, and deployment patterns emerge regularly. Our team maintains technical expertise across major cloud platforms and stays current with developments in GPU computing, containerization, and ML workflow tooling. This allows us to recommend approaches grounded in current capabilities rather than theoretical possibilities.

We value transparency in our client relationships. Infrastructure projects involve technical tradeoffs between cost, performance, and operational complexity. Our consultations include honest discussions about these tradeoffs, helping clients make informed decisions aligned with their specific constraints and priorities.

Our Team

Infrastructure specialists with deep expertise in cloud architecture and machine learning operations

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Kelvin Lim

Principal Cloud Architect

Kelvin leads our infrastructure design practice with over twelve years of experience building distributed systems. He specializes in GPU cluster configuration and high-throughput data pipeline architecture for AI workloads.

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Sarah Chen

MLOps Engineering Lead

Sarah focuses on ML pipeline automation and deployment infrastructure. Her background in software engineering and data science informs her approach to building reliable MLOps workflows that teams can operate confidently.

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Ahmad Rahman

Infrastructure Operations Specialist

Ahmad manages platform monitoring, capacity planning, and operational runbook development. He ensures that the infrastructure we deploy remains performant and cost-effective as client workloads evolve over time.

Our Approach to Infrastructure Quality

Professional standards and practices that ensure reliable, secure, and maintainable AI platforms

Security Architecture

Infrastructure designs implement network segmentation, encryption, identity management, and audit logging aligned with cloud security frameworks and client compliance requirements.

Infrastructure as Code

All platform configurations are version-controlled and reproducible through declarative templates. This ensures consistency across environments and enables systematic change management.

Monitoring and Observability

Comprehensive instrumentation for compute utilization, job execution, data pipeline health, and cost tracking. Teams receive dashboards and alerting configured for their operational needs.

Documentation Standards

Platform implementations include architecture diagrams, configuration references, operational runbooks, and troubleshooting guides that enable internal teams to manage systems effectively.

Knowledge Transfer

Implementation projects include hands-on training sessions for client technical teams covering platform operation, common workflows, performance optimization, and incident response procedures.

Cost Optimization

Infrastructure assessments include analysis of compute and storage costs. We configure resource quotas, implement autoscaling policies, and establish cost monitoring to help teams manage cloud spending.

Infrastructure Expertise for AI Workloads

Deploying AI systems requires infrastructure that can handle computationally intensive training jobs, manage large datasets efficiently, and support reliable model serving at scale. Our technical team brings practical experience across these domains, having implemented platforms for organizations running diverse AI workloads.

GPU cluster management represents a specialized area within cloud infrastructure. We configure job schedulers, implement resource quotas, optimize CUDA libraries, and establish monitoring for GPU utilization metrics. This expertise helps teams make effective use of expensive compute resources and avoid common bottlenecks in distributed training workflows.

Data pipeline architecture is fundamental to ML platform success. We design ingestion systems, storage tiers for different access patterns, transformation workflows, and versioning mechanisms that ensure training data flows efficiently through development cycles. Our implementations address data lineage tracking, quality validation, and feature engineering at appropriate points in the pipeline.

MLOps automation requires adapting continuous integration and deployment practices to the unique characteristics of machine learning workflows. We configure experiment tracking, model registry systems, validation gates, deployment mechanisms, and rollback procedures that allow teams to move models from development to production with appropriate governance controls.

Cloud platform selection involves evaluating compute options, storage services, networking capabilities, and managed ML tools across AWS, Azure, and GCP. Our assessments consider technical requirements, existing infrastructure investments, team expertise, and cost implications to recommend platforms aligned with client circumstances.

Security implementation for AI infrastructure extends beyond standard cloud security practices. We address concerns specific to ML workloads including training data access controls, model artifact protection, API authentication for serving endpoints, and audit logging for compliance requirements. Configurations follow cloud security frameworks while accommodating the collaborative nature of data science work.

Platform scalability planning requires understanding how workload characteristics change as teams grow and models become more complex. We design architectures that can accommodate increasing numbers of concurrent training jobs, larger datasets, and more sophisticated deployment patterns without requiring fundamental redesigns.

Discuss Your Infrastructure Requirements

Our team is available to explore how AI infrastructure can support your technical objectives. We start with understanding your current environment and the capabilities you need to develop.

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