YTJK Systems

Cloud & AI, architected for business.

Architecture and engineering for cloud platforms with AI built in, focused on delivery speed, cost control, resilience, security and compliance, including regulated environments.

Users & APIs Kubernetes autoscaled cluster Compute node pool Model serving Data & models storage, registry Observability

Challenges addressed

Recurring challenges in cloud and AI programmes, and the focus of every YTJK Systems engagement.

Costs growing faster than the business

Cloud and AI spend nobody can explain, over-provisioned platforms and no link between cost and value.

AI pilots that never reach production

Promising prototypes stuck on security, cost, evaluation and the question of who runs them.

Delivery slowed by the platform

Teams waiting on environments, manual releases and a platform not ready for AI workloads.

Modernisation that has stalled

Migrations half-finished, legacy and cloud both running, and costs doubled in the meantime.

Security, compliance and regulatory pressure

Regulatory requirements, audit evidence, repeat incidents and AI risk the board is asking about.

No clear cloud or AI strategy

Decisions made project by project, with no roadmap leadership and engineers both trust.

Engagements

Defined engagements with clear deliverables. Scope and timeline are agreed for each engagement, based on the size and complexity of the change.

Advise

Direction, assessment and ongoing architecture leadership.

Cloud and AI strategy

Current position, target state and the sequence to get there, with AI adoption and data foundations planned alongside the platform.

  • Current-state assessment of platforms, data and AI use cases
  • Target cloud, data and AI architecture
  • Prioritised roadmap with business case and investment profile
  • Cloud and AI initiatives aligned to business priorities
  • Clear decisions on what to build, buy or retire
  • A plan leadership can fund, track and explain

Cloud and AI architecture review

An independent assessment of an existing platform and its AI workloads: risks, readiness and spend.

  • Findings and risk register
  • Cost-saving opportunities across cloud and AI, sized
  • Security, resilience and compliance gaps identified
  • Quick wins and a remediation plan
  • Reduced operational and audit risk
  • Spend linked to business value

Architecture leadership

Ongoing design authority and direction across cloud, data and AI.

  • Design authority and architecture reviews
  • Vendor, model and build-or-buy decisions
  • Architecture standards and governance
  • Consistent decisions across programmes and teams
  • Lower delivery risk on major initiatives
  • Advice for leadership and the board

Build

Design and delivery of platforms, and modernisation of existing estates.

Cloud and AI platform delivery

Design and build of cloud and Kubernetes platforms with data and AI capability built in, alongside internal teams.

  • Platform built as code, ready for AI workloads
  • AI services in production with evaluation, monitoring and cost controls
  • Resilience by design: availability, recovery and disaster recovery targets met
  • Faster, safer releases through automation and paved paths
  • Predictable running costs at real volumes
  • Capability that stays with internal teams

Migration and modernisation

Legacy applications and infrastructure moved or modernised, so the estate is ready for cloud and AI.

  • Application and infrastructure portfolio assessment
  • Migration strategy per workload: rehost, refactor, replace or retire
  • Wave plan with dependencies, risks and cut-over approach
  • Modernised, AI-ready platforms and data
  • Reduced legacy cost and technical debt
  • Programmes completed rather than left running in parallel

Assure

Security, compliance and cost kept under control as platforms and AI scale.

Security, governance and compliance

Cloud security architecture and AI governance that stand up to regulators, auditors and the board.

  • Security architecture across identity, network, data and workloads
  • AI governance: model risk, data use, evaluation and human oversight
  • Controls mapped to frameworks such as ISO 27001, DORA and the EU AI Act
  • Evidence that is generated continuously, not assembled for audits
  • Lower regulatory and reputational risk
  • Faster approvals for new services and AI use cases

Cost optimisation

A structured FinOps programme that reduces cloud and AI spend and keeps it under control.

  • Cost visibility by product, team and AI service
  • Right-sizing, commitment and architecture savings
  • Unit economics such as cost per customer or per AI request
  • Budgets, alerts and accountability built into delivery
  • Lower, predictable run costs
  • Savings that are sustained, not one-off

Business outcomes

Faster time to market

Shorter path from idea to production for products and AI features alike.

Lower, predictable cost

Cloud and AI spend tied to value, with the visibility to keep it that way.

Reduced operational risk

Resilient, secure, observable platforms and AI services that stand up to audits and incidents.

Value that is measured

Platforms and AI services tracked on quality, cost, adoption and business impact.

Confident decisions

A cloud and AI strategy that leadership can explain, fund and track.

Capability that stays

Internal teams own the result, with documentation and hand-over built in.

How an engagement runs

Four stages, each ending in an agreed output. The depth of each stage scales with the size, complexity and risk of the change.

  1. Discover

    Interviews with business, engineering, security and finance stakeholders. Review of the current architecture, cloud spend, incident history, delivery metrics and constraints such as regulation, contracts and skills. Goals and measures of success agreed.

    Output: discovery report with findings, risks and priorities, and a scoped proposal.

  2. Design

    Options assessed against cost, risk and time to value. Target architecture defined, with security and compliance reviewed and key decisions recorded as architecture decision records. Roadmap sequenced to deliver value early.

    Output: target architecture, decision log and roadmap, signed off by stakeholders.

  3. Deliver

    Incremental delivery in short iterations, built as code alongside internal teams. Regular demonstrations, progress tracked against the agreed measures, and risks, dependencies and change managed openly.

    Output: working platform or service, released in stages.

  4. Hand over

    Runbooks, operating model, training and knowledge transfer so internal teams can run and evolve the result. A review against the original success measures closes the engagement.

    Output: documented, owned service and recommendations for next steps.

Principles

Outcome-based

Success measures, deliverables and responsibilities are agreed before work starts and reported throughout.

Independent

No reseller ties. Recommendations across AWS, Azure and Google Cloud are based only on requirements.

Business case first

Every recommendation comes with its cost, risk and expected benefit.

Secure and compliant by design

Security, data protection and regulatory controls are designed in from the start, with the evidence regulated environments require.

Cloud and AI together

AI systems are designed with the platform, security and operations they depend on.

Knowledge transfer

Documentation and hand-over are part of every engagement, so capability stays in-house.

Insights

Architecture notes from YTJK Systems, and key public references for cloud and AI system design.

Architecture notes

Reference architecture: retrieval-augmented generation (RAG)AI system design

RAG grounds a language model in an organisation's own content. It is two systems: an ingestion pipeline that keeps an index fresh, and a query path that has to be fast, safe and observable.

IngestionPreparing structured and unstructured data for retrieval.
Unstructured sourcesDocuments, PDFs, wiki and web pages, images, audio, video
Unstructured data pathChunked, embedded and indexed
  1. Parse & extractOCR, transcription, dedup, quality checks
  2. ClassifyType, topic, sensitivity, PII redaction
  3. Chunk & enrichStructure-aware chunking, summaries
  4. Metadata & accessProvenance, filters, ACLs per chunk
  5. EmbeddingsChunks to vectorsversioned model
  6. Search indexVector + keyword
Supports semantic and hybrid retrieval
→RetrievalRouting, hybrid search, re-ranking, generation
Structured sourcesDatabases, tables, data warehouse, data catalogue
Structured data pathQueried in place, under governance
  1. Schema & profilingTables, fields, relationships, quality checks
  2. Classify & describeDomain, sensitivity, ownership, business descriptions
  3. Governed query layerSQL access, views, row and column policies
  4. Structured accessExact lookups, aggregations, analytical queries
No row-level embeddings; schema descriptions indexed for routing
Shared ingestion controls
  • Orchestration
  • Validation
  • Incremental sync and schedules
  • Lineage and provenance
  • Access control
  • Lifecycle, re-indexing and deletion
Query pathAnswering a request with permission-aware, grounded retrieval.
Online request flowFast, safe and observable
  1. User or appQuestion or task
  2. API gatewayAuthentication, quotas, rate limits
  3. Input guardrailsPrompt injection, PII, policy checks
  4. RouterUnstructured, structured or both
  5. RetrieveHybrid search and governed SQL, filtered by the caller's permissions
  6. Re-rankMost relevant context first
  7. GenerateGrounded answer with citations and output checks
Permissions enforced at retrieval: the model never sees content the user cannot
Observability and evaluation
  • End-to-end tracing
  • Retrieval quality
  • Answer faithfulness
  • Latency per stage
  • Cost per request
  • User feedback loop

Key design decisions

  • Chunking: split on document structure, not fixed sizes, and keep source and section metadata for citations and access control.
  • Hybrid retrieval: combine vector and keyword search, then re-rank. Pure vector search misses exact terms such as product codes.
  • Permissions: filter by the caller's entitlements at retrieval time, so the model never sees content the user cannot.
  • Versioning: treat the embedding model and index as a versioned pair. Changing one means re-indexing.
  • Evaluation: keep a golden question set and measure retrieval quality and answer faithfulness on every change.
Designing a multi-tenant Kubernetes platformPlatform architecture

Multi-tenancy is a spectrum of isolation, not a single choice. The right level depends on how much control each tenant needs and how much separation the business requires, and most platforms use more than one level at once.

Isolation levelsChosen per workload; most platforms use more than one.
  1. NamespaceOne per application and environment, e.g. payments-prodSoft isolation, lowest cost
  2. TenantSelf-service namespaces within a team's quotaTeam autonomy on shared clusters
  3. Virtual clusterOwn API server, CRDs, operators and admin rights on shared nodesControl-plane isolation
  4. Dedicated clusterRegulated data, separate versions or distinct scaling such as GPUHard isolation, highest cost

More isolation, more cost and operational effort

Tenant onboarding as codeEvery tenant starts secure and consistent
  1. Tenant definitionDeclared in Git: owners, environments, quotas
  2. Review and policyPull request, automated policy checks
  3. GitOps controllerReconciles the desired state
  4. Guardrails appliedNamespaces, group RBAC, quotas, network and admission policies
  5. Golden pathsService, pipeline and dashboard templates
Shared platform services
  • Ingress and DNS
  • Certificates
  • Secrets
  • Policy engine
  • Observability
  • Cost attribution

Key design decisions

  • Isolation per workload, not per platform: namespaces per application and environment by default; a tenant layer where teams need self-service; virtual clusters when teams need cluster-scoped resources; dedicated clusters only for regulatory separation, different Kubernetes versions or distinct scaling profiles such as GPU workloads.
  • Onboarding as code: one tenant definition creates namespaces, group-based RBAC, quotas and limit ranges, network policies and admission policies, so every tenant starts secure and consistent.
  • Data-plane isolation on shared nodes: default-deny networking, dedicated node pools or taints for sensitive and GPU workloads, and sandboxed runtimes where the risk justifies it.
  • Golden paths: templates for a service, its pipeline and its dashboards, so the secure default is also the easiest option.
  • Cost attribution: tenant, application and cost-centre labels from day one, so spend can be shown back to the business.
Serving LLMs in production: latency, cost and reliabilityAI platform architecture

Once an AI feature has real users, the model call becomes a dependency like any other: it needs routing, limits, fallbacks and monitoring. A model gateway puts that in one place.

Model gatewayOne control point between applications and every model.
ApplicationsProducts, internal tools, agents
Model gateway
  • Access · authentication, per-team quotas and budgets
  • Safety · input and output filters, PII handling
  • Cache · repeated prompts and responses
  • Routing · by task, cost and latency
  • Resilience · timeouts, retries, fallback
  • Managed model APIPrimary provider
  • Managed model APIFallback provider or region
  • Self-hosted modelsAutoscaled on queue depth, for residency or unit cost
Operations
  • Tokens and cost per team
  • Time to first token
  • Tokens per second
  • Error and fallback rates
  • Quality evaluation
  • Audit logs

Key design decisions

  • Route by task: send simple tasks to smaller, cheaper models and reserve the largest models for work that needs them.
  • Measure the right latency: track time to first token and tokens per second, not just total request time.
  • Plan for failure: timeouts, retries with backoff and a fallback model or provider, so one outage does not take the feature down.
  • Control spend: per-team budgets, caching of repeated prompts and alerts on cost per request.
  • Build or buy: self-host only when data residency, unit cost at scale or customisation justify the operational load.

Reference library

Cloud architecture

Platforms and system design

AI and ML architecture

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