Services

KubeStaff provides hands-on consulting and delivery for AI-ready cloud platforms and production-grade AI systems.
Engagements focus on architecture, delivery, and operational enablement under real-world enterprise constraints.

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AI Platform & Infrastructure Delivery

Design and delivery of cloud-native platforms that enable AI workloads to run reliably, securely, and at scale.

This service is suitable when organizations need a solid foundation for AI initiatives rather than isolated experiments.

What this includes

• Kubernetes-based platform design and implementation (AKS, EKS, on-prem)
• Infrastructure as Code using Terraform
• GitOps-based delivery using Argo CD
• Secure storage, secrets management, and identity integration (SSO / OIDC)
• CI/CD pipelines for services, data pipelines, and AI workloads
• Autoscaling, replication, and traffic management via ingress controllers
• Production observability across metrics, logs, and alerts

Typical outcomes

• A production-ready AI platform operated by internal teams
• Reduced operational risk and manual intervention
• Clear ownership, documentation, and handover

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LLM & RAG Platform Engineering

Design and implementation of LLM-based platforms that deliver accurate, up-to-date, and governed responses using enterprise data.

This service is for organizations moving beyond demos toward customer-facing or internal AI systems.

What this includes

• Retrieval-Augmented Generation (RAG) architecture and implementation
• Near real-time knowledge ingestion from:
  • websites
  • documents and policies
  • product catalogs and events
  • CRM and internal resource management systems
• Centralized, self-hosted prompt management with versioning and governance
• Secure access to multiple LLM providers with policy and quota control
• Observability, cost awareness, and usage tracking

Typical outcomes

• Accurate, context-aware AI responses grounded in business data
• Controlled rollout of prompt and model changes
• A reusable platform foundation for future AI use cases

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Computer Vision Platforms (CVAT & Production CV)

Design and delivery of computer vision platforms for data annotation, model iteration, and production operations.

This service is relevant for retail, manufacturing, and AI teams working with image- or video-based models.

What this includes

• Kubernetes-based CV platforms centered around open-source CVAT
• Enterprise extensions for authentication, access control, and partner access
• Secure, compliant storage for datasets and artifacts
• CI/CD pipelines for CV platforms and extensions
• Autoscaling, observability, and hardened production operations
• Integration with model training and inference workflows

Typical outcomes

• Stable, scalable computer vision platforms operated in production
• Faster onboarding of internal and external annotation teams
• Reduced operational overhead for AI teams

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AI Agent & System Architecture

Architecture and design of agent-based systems that integrate safely into existing production environments.

This service focuses on structure, responsibility boundaries, and operability, not hype.

What this includes

• Agent responsibility and interaction design
• Safety, governance, and observability patterns
• Integration with existing services, data sources, and workflows
• Rollout strategies from MVP to production
• Reference implementations and architecture documentation

Typical outcomes

• Clear, maintainable agent architectures
• Reduced risk when introducing AI into existing products
• Faster alignment between engineering, product, and operations

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Cloud Migration & Platform Modernization

Migration of legacy systems to cloud-native, Kubernetes-based platforms with a focus on reliability and operability.

What this includes

• Assessment of existing systems and constraints
• Migration planning and phased rollout strategies
• Kubernetes-native redesign where appropriate
• Observability, autoscaling, and resilience improvements
• Operational documentation and team enablement

Typical outcomes

• Modernized platforms ready for AI and data workloads
• Improved reliability and scalability
• Clear operational ownership

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How engagements typically work

1. Discovery
   Short, focused workshops to clarify goals, constraints, and success criteria.

2. Architecture and plan
   Concrete target architecture, delivery plan, and milestone-based backlog.

3. Delivery
   Hands-on implementation using Infrastructure as Code, GitOps, and production best practices.

4. Handover
   Documentation, runbooks, onboarding sessions, and optional ongoing support.

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Engagement models

• Architecture and advisory
• Hands-on delivery
• Interim platform engineering
• Project-based or retainer engagements

All engagements are structured to leave teams stronger and more autonomous.

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