Maayan AI Infrastructure is built to power the next era of enterprise intelligence—where AI is not an isolated innovation project, but a core operating capability. As organizations adopt GenAI, computer vision, predictive analytics, and autonomous workflows, infrastructure becomes the difference between “ideas in notebooks” and “AI in production.” Without the right foundation, teams face slow training cycles, unstable deployments, data bottlenecks, underutilized GPUs, security risk, and unpredictable cost.
Maayan Technologies helps clients build Live Enterprises—organizations that are AI-powered, digitally agile, continuously learning, and resilient. From an infrastructure perspective, a Live Enterprise needs a platform that can ingest and govern data continuously, train and serve models reliably, scale capacity as demand grows, and maintain strict security and compliance. AI infrastructure is not just hardware. It is an engineered stack that includes compute, storage, networking, orchestration, observability, security, governance, and operational readiness.
Many enterprises are also navigating a difficult balance:
Performance vs cost: GPUs are expensive; infrastructure must be sized for workload and utilized effectively.
Speed vs governance: teams need agility, but leadership needs controls, audit trails, and compliance.
Cloud vs on-prem vs hybrid: data gravity, latency, regulation, and cost constraints vary.
Experimentation vs production: training and experimentation environments are different from reliable inference platforms.
Maayan AI Infrastructure addresses these realities with a pragmatic, outcome-driven approach. We design and implement AI environments that support the full lifecycle—data pipelines, training, fine-tuning, retrieval systems, inference, monitoring, and continuous improvement—across on-prem, cloud, or hybrid architectures. The result is infrastructure that accelerates innovation while remaining secure, scalable, and measurable.
We start with your business goals and translate them into technical architecture and capacity planning.
AI workload discovery: training, fine-tuning, inference, batch scoring, RAG, agents, vision, edge
Capacity planning: GPU/CPU requirements, memory, network bandwidth, storage throughput, growth forecast
TCO modeling: CAPEX/OPEX tradeoffs, cloud vs on-prem comparisons, utilization assumptions
Reference architecture: data layer, compute layer, orchestration, security, observability, governance
Roadmap: phased scaling from pilot to enterprise multi-team platform
Deliverables: infrastructure blueprint, sizing and BOM guidance, architecture diagrams, phased roadmap, TCO model.
We design compute foundations that match your workload patterns and budget.
GPU server design: single-node workstations, multi-GPU servers, multi-node training clusters
Virtualization and isolation: tenant separation for multiple teams, project-based access
Containerization readiness: optimized runtimes for AI frameworks and inference services
Scheduler strategy: fair-share scheduling, quota enforcement, priority lanes for production inference
Performance tuning: CPU-to-GPU balance, PCIe considerations, memory bandwidth planning
Outcomes: faster training, predictable inference latency, improved utilization.
AI performance is often limited by networking and data movement. We engineer low-latency and high-throughput networks tailored to your environment.
Fabric design: high-speed Ethernet or InfiniBand architectures (depending on workload)
Spine-leaf topology and scaling approach
Transceivers, cabling strategy, rack adjacency design, redundancy planning
Network segmentation and security: east-west traffic control, isolation by environment and team
Quality-of-service planning: prioritize inference and critical services
Outcomes: reduced training time, stable distributed workloads, consistent multi-node performance.
AI needs storage that can feed GPUs fast and manage massive datasets reliably. We design data tiers that balance performance and cost.
Storage tiers: NVMe local scratch, shared high-performance storage, object storage for long-term datasets
Data lifecycle management: ingest, versioning, access controls, retention, archival
Backup and recovery: snapshots, immutable backups (when needed), disaster recovery options
Dataset governance: cataloging, lineage, dataset versioning, reproducibility support
Throughput optimization: parallel I/O planning, caching, and dataset staging strategies
Outcomes: fewer data bottlenecks, faster experiments, improved reproducibility.
AI infrastructure must be operationally manageable. We implement the foundation required to deploy and run AI reliably.
Kubernetes and cluster management (where appropriate)
Model serving foundations: scalable inference endpoints, autoscaling patterns, traffic shaping
MLOps foundations: model registry, artifact storage, CI/CD integration, approvals and release workflows
LLMOps foundations: prompt management, evaluation harnesses, retrieval pipelines monitoring, feedback loops
Environment management: dev/test/prod separation, sandbox spaces for research, production controls
Outcomes: faster deployment cycles, reduced operational risk, consistent delivery patterns.
Maayan designs AI infrastructure with enterprise security as a baseline—not an afterthought.
Identity and access management: RBAC, SSO integration, least privilege design
Secret management: keys/tokens rotation, secure storage, audit trails
Data protection: encryption at rest and in transit, secure endpoints, network policies
Governance enforcement: environment boundaries, approvals for production deployments
Compliance readiness: logging, monitoring, and documentation patterns aligned to organizational needs
Outcomes: reduced risk of data leakage, stronger compliance posture, improved trust for scaling AI.
Infrastructure without observability becomes expensive and fragile. We implement operational visibility and reliability practices.
GPU utilization dashboards, job queue insights, storage throughput monitoring
Application-level observability: latency, error rates, model performance monitoring hooks
Alerts and incident workflows: thresholds, escalation, and SRE-friendly runbooks
Capacity reporting and forecasting
Reliability patterns: redundancy, failover strategies, patch management guidance
Outcomes: higher uptime, better utilization, predictable costs, and faster troubleshooting.
Many organizations require a hybrid approach: sensitive workloads on-prem with burst capacity in cloud, or multi-cloud for resilience and vendor flexibility.
Hybrid design patterns: consistent orchestration across environments, secure connectivity
Cloud bursting strategy: cost thresholds, workload policies, data movement strategy
Data residency and governance enforcement across environments
Consistent MLOps/LLMOps toolchain across on-prem and cloud
Outcomes: flexibility without chaos, consistent governance, controlled cost expansion.
For enterprises, universities, and innovation centers, Maayan provides a structured AI lab setup approach.
Multi-user environment design: quotas, project spaces, access governance
Education and experimentation zones with safe policies
Training and enablement for administrators and users
Standardized onboarding and documentation
Scale path from lab to production enterprise workloads
Outcomes: faster onboarding, stable multi-team collaboration, and sustainable operations.
We don’t build infrastructure as a static asset. We build it as a living system that supports continuous learning, fast iteration, and resilient operations. Our designs include feedback loops—utilization analytics, performance monitoring, and capacity planning—so infrastructure evolves with business needs.
Many solutions fail because they optimize only one layer (e.g., GPUs) while ignoring network, storage, or scheduling. Maayan designs for end-to-end throughput:
balanced compute-to-I/O architecture
right network topology and cabling strategy
storage tiers that feed GPUs efficiently
scheduling that prevents idle resources
This approach reduces “hidden bottlenecks” and improves real-world performance.
AI infrastructure is expensive. We focus on making it pay back faster by maximizing utilization:
fair-share and quota models for multiple teams
scheduling patterns for research and production coexistence
monitoring and dashboards for accountability
policy-based resource allocation
This ensures GPUs aren’t sitting idle and budgets aren’t wasted.
Maayan designs secure-by-default AI foundations with auditability and enforceable boundaries—especially important for GenAI and enterprise data access. Our approach supports fast innovation while keeping leadership confident about risk.
We deliver infrastructure that teams can operate day-to-day:
documented architecture and configuration baselines
operational SOPs and runbooks
monitoring and alerting setups
admin and user training
onboarding playbooks for new projects and teams
This reduces operational dependence and strengthens long-term stability.
We align platform choices to workload and context—on-prem, cloud, hybrid; Ethernet or InfiniBand; storage tiers; orchestration tools—without pushing unnecessary complexity. The outcome is infrastructure that fits your real constraints and scales responsibly.
Maayan AI Infrastructure produces measurable outcomes across performance, cost, reliability, and organizational capability.
Faster training and fine-tuning through reduced compute/network/storage bottlenecks
Lower inference latency and improved stability for customer-facing AI services
Higher throughput for data pipelines and retrieval systems (RAG workloads)
Improved GPU utilization through scheduling, monitoring, and governance
Reduced cost per experiment with shared services and standardized environments
Controlled cloud spending through hybrid policies and workload placement strategies
Improved uptime through observability, redundancy, and operational playbooks
Faster incident resolution with clear dashboards and runbooks
Reduced “production surprises” through validation and readiness checklists
Stronger data protection via encryption, access controls, and network segmentation
Reduced GenAI risk with controlled access to enterprise knowledge and audit trails
Compliance-ready logging and governance models
Faster onboarding of new teams and projects with repeatable platform patterns
Internal enablement through training and structured handover
A scalable foundation that supports multiple AI products and business functions
Maayan AI Infrastructure is designed for organizations that want outcomes fast, without sacrificing trust, security, or scalability.
At Maayan Tech, we are committed to providing exceptional service and support. Your questions, feedback, and inquiries are important to us, and we will do our best to respond promptly. Whether you’re a business seeking technology solutions or an individual with a query, we are here to assist you.
