Architecture and engineering for cloud platforms with AI built in, focused on delivery speed, cost control, resilience, security and compliance, including regulated environments.
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.
Regulatory requirements, audit evidence, repeat incidents and AI 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, deliverables and responsibilities 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.
Security, data protection and regulatory controls are designed in from the start, with the evidence regulated environments require.
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.
Ingestion and query paths for grounding language models in organisational content, with access control, evaluation and cost in view.
Read the note → Platform architectureDesigning a multi-tenant Kubernetes platformIsolation tiers, tenant onboarding as code and shared platform services for a Kubernetes platform used by many teams.
Read the note → AI platform architectureServing LLMs in production: latency, cost and reliabilityA model gateway, routing, caching and capacity choices that balance latency, cost and reliability for LLM workloads.
Read the note →Share a few details about the requirement. A reply follows within two working days to arrange a call.