Architecture and engineering for cloud platforms with AI built in, focused on delivery speed, cost control, resilience and safe use of AI in production.
Recurring challenges in cloud and AI programmes, and the focus of every YTJK Systems engagement.
Cloud and AI spend nobody can explain, over-provisioned platforms and no link between cost and value.
Promising prototypes stuck on security, cost, evaluation and the question of who runs them.
Teams waiting on environments, manual releases and a platform not ready for AI workloads.
Migrations half-finished, legacy and cloud both running, and costs doubled in the meantime.
Incidents that repeat, controls that are hard to evidence and risk the board is asking about.
Decisions made project by project, with no roadmap leadership and engineers both trust.
Defined engagements with clear deliverables. Scope and timeline are agreed for each engagement, based on the size and complexity of the change.
Direction, assessment and ongoing architecture leadership.
Current position, target state and the sequence to get there, with AI adoption and data foundations planned alongside the platform.
An independent assessment of an existing platform and its AI workloads: risks, readiness and spend.
Ongoing design authority and direction across cloud, data and AI.
Design and delivery of platforms, and modernisation of existing estates.
Design and build of cloud and Kubernetes platforms with data and AI capability built in, alongside internal teams.
Legacy applications and infrastructure moved or modernised, so the estate is ready for cloud and AI.
Security, compliance and cost kept under control as platforms and AI scale.
Cloud security architecture and AI governance that stand up to regulators, auditors and the board.
A structured FinOps programme that reduces cloud and AI spend and keeps it under control.
Shorter path from idea to production for products and AI features alike.
Cloud and AI spend tied to value, with the visibility to keep it that way.
Resilient, secure, observable platforms and AI services that stand up to audits and incidents.
Platforms and AI services tracked on quality, cost, adoption and business impact.
A cloud and AI strategy that leadership can explain, fund and track.
Internal teams own the result, with documentation and hand-over built in.
Four stages, each ending in an agreed output. The depth of each stage scales with the size, complexity and risk of the change.
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.
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.
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.
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.
Success measures are agreed before work starts and reported throughout.
No reseller ties. Recommendations across AWS, Azure and Google Cloud are based only on requirements.
Every recommendation comes with its cost, risk and expected benefit.
Deliverables, responsibilities and commercials are agreed before work starts.
AI systems are designed with the platform, security and operations they depend on.
Documentation and hand-over are part of every engagement, so capability stays in-house.
Architecture notes from YTJK Systems, and key public references for cloud and 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.
INGESTION (batch / event-driven)
Sources ─▶ Parse & clean ─▶ Chunk ─▶ Embed ─▶ Vector + keyword index
(docs, wiki, DB) ▲ versioned embedding model
QUERY PATH (online)
User ─▶ API gateway ─▶ Guardrails ─▶ Retriever ─▶ Re-ranker ─▶ LLM ─▶ Response
(auth, quotas) (input) (hybrid) │ (citations)
▼
Traces, evals, cost per request
Key design decisions
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
▲
│ Dedicated cluster regulated data, separate versions, own blast radius
│ Virtual cluster own CRDs, operators and admin rights on shared nodes
│ Tenant self-service namespaces within a team's quota
│ Namespace one per application and environment, e.g. payments-prod
└──────────────────────────────────────────────────────▶ Cost and operational effort
Tenant definition (Git) ─▶ GitOps ─▶ namespaces, RBAC, quotas, network and admission policy
Key design decisions
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.
Apps ─▶ Model gateway ───────────────┬─▶ Managed model API (primary)
├─ auth, per-team quotas ├─▶ Managed model API (fallback)
├─ prompt / response cache └─▶ Self-hosted models
├─ routing by task & cost (autoscaled on queue depth)
└─ logging: tokens, latency, cost, errors
Key design decisions