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Services

AI infrastructure services for secure production adoption

Kishin Technologies helps European companies design and build the technical foundation required to use AI safely at scale.

The work focuses on secure model access, multi-cloud architecture, private inference, RAG systems, observability, cost control, and governance readiness.

These are productized technical services designed to produce concrete deliverables, not vague strategy documents.

First EngagementStart Here

AI Platform Readiness Sprint

A focused 1–2 week engagement that gives your organization a clear technical roadmap for secure AI adoption.

Best For

  • ✓Companies with active AI pilots
  • ✓Teams using multiple AI tools without central visibility
  • ✓Cloud teams asked to support AI without a reference architecture
  • ✓Leadership needing a clear risk and implementation plan
  • ✓Organizations preparing for secure internal AI platforms

What's Included

  • →Stakeholder and technical discovery
  • →Review of current cloud environment
  • →Review of current AI usage and tools
  • →Data risk and security assessment
  • →AI access control and governance gap analysis
  • →Cost visibility and FinOps review
  • →Architecture options across AWS, Azure, GCP, and private infrastructure
  • +3 more items

Deliverables

  • ✓AI platform readiness report
  • ✓Risk and gap analysis
  • ✓Target architecture diagram
  • ✓Implementation roadmap
  • ✓Cost and governance recommendations
  • ✓Follow-up implementation proposal
Duration: 1–2 weeksStart with a Readiness Sprint
Implementation

Multi-Cloud AI Gateway MVP

A secure gateway that gives your organization one controlled access point to AI models across selected providers.

Best For

  • ✓Companies using multiple LLM providers
  • ✓Teams needing usage control and cost visibility
  • ✓Product teams adding AI features
  • ✓Organizations that want to avoid provider lock-in
  • ✓Companies moving from direct API usage to governed access

What's Included

  • →Gateway architecture design
  • →Custom LiteLLM deployment and configuration
  • →Provider integration with selected platforms
  • →Authentication and authorization
  • →API access patterns
  • →Model routing
  • →Usage logging
  • +6 more items

Deliverables

  • ✓Working AI gateway MVP
  • ✓API documentation
  • ✓Deployment repository
  • ✓Logging and usage dashboard
  • ✓Security and operations notes
  • ✓Recommended next-phase roadmap

Provider Options

Azure OpenAI / Azure AI FoundryAWS BedrockGCP Vertex AIOpenAI-compatible APIsPrivate or sovereign inference endpoints where data residency requires it
Duration: 2–4 weeksDiscuss an AI Gateway MVP
Implementation

Secure RAG Platform Foundation

A secure foundation for internal knowledge assistants and retrieval-augmented generation systems.

Best For

  • ✓Companies building internal AI assistants
  • ✓Organizations with private documentation or knowledge bases
  • ✓Teams concerned about data leakage
  • ✓Companies needing access-aware retrieval
  • ✓Product teams adding AI search or assistant capabilities

What's Included

  • →RAG architecture design
  • →Document ingestion pipeline
  • →Chunking and metadata strategy
  • →Vector database recommendation
  • →Retrieval API design
  • →Access control approach
  • →Model and embedding provider selection
  • +4 more items

Deliverables

  • ✓Secure RAG architecture
  • ✓Working foundation or MVP
  • ✓Ingestion and retrieval pipeline
  • ✓Infrastructure documentation
  • ✓Evaluation and monitoring plan
  • ✓Security recommendations
Duration: 3–6 weeks depending on scopePlan a Secure RAG Platform
Audit

AI FinOps & Cost Control Audit

A focused audit for companies already spending money on AI APIs, cloud AI services, or inference infrastructure.

Best For

  • ✓Companies with growing or unclear AI spend
  • ✓Organizations needing cost attribution by team or project
  • ✓Teams evaluating open-source vs. commercial model trade-offs
  • ✓Engineering teams without AI cost dashboards

Focus Areas

  • →Usage visibility
  • →Cost allocation
  • →Model selection
  • →Prompt and token optimization
  • →Routing strategy
  • →Quotas
  • →Budget controls
  • →Cloud-native cost dashboards
  • →Open-source/private inference tradeoffs
Duration: 1–2 weeksReview AI Costs
Implementation

Private AI / Hybrid Inference Deployment

Design and implementation support for organizations whose data sovereignty, compliance, or latency requirements rule out standard cloud AI APIs.

Best For

  • ✓Organizations with data sovereignty or residency requirements
  • ✓Regulated industries that must keep inference within controlled infrastructure
  • ✓Teams with strict latency or cost requirements at scale
  • ✓Organizations building hybrid cloud/private inference setups

Focus Areas

  • →Data residency and sovereignty requirements
  • →Compliance-driven architecture decisions
  • →Self-hosted / open-source model serving (e.g. vLLM)
  • →GPU infrastructure and containerized deployment
  • →Model selection and security boundaries
  • →Observability and cost/performance tradeoffs
  • →Integration with AI gateways
Duration: 2–4 weeks depending on scopeExplore Private AI Infrastructure

Not sure where to start?

Most companies should begin with the AI Platform Readiness Sprint. It creates the technical roadmap before implementation begins.

Start with a Readiness SprintContact Us