Focused engineering engagements

Cloud & AI Engineering. Packaged for Delivery.

Tongtech connects cloud foundations, platform engineering and production AI across cloud providers through senior-led engineering packages. Each starts with a bounded problem and a clear scope, technical deliverables and acceptance checks, then hands over code and operating guidance your team can use.

Cloud foundations

Establish a governed cloud baseline and plan workloads against clear ownership, access and network boundaries.

  • Landing-zone architecture and implementation
  • Cloud account, project or subscription structure, identity, networking and governance
  • Infrastructure as Code, migration planning and operational handover
Explore Cloud Foundation

Platform engineering

Give engineering teams repeatable paths to provision, release and operate workloads with appropriate controls.

  • Reusable Terraform and Bicep modules
  • CI/CD, GitOps, managed Kubernetes and workload onboarding
  • Identity, validation, observability and runbooks
Explore Platform Engineering

Enterprise AI

Move selected AI workloads toward production with governed infrastructure, controlled data access and operational readiness.

  • Secure AI foundations, private connectivity and managed identity
  • RAG and bounded agent application patterns
  • Evaluation, release controls, observability and cost visibility
Explore Enterprise AI

A practical path from review to running system

Each step leaves you with something usable.

The work can start at any layer. We identify the constraint, agree a bounded outcome, and connect the design to implementation and operations.

  1. 01

    Assess

    Review architecture, delivery practices and constraints; prioritize the decisions that matter.

  2. 02

    Design

    Turn findings into a target pattern, implementation boundary and sequence your team can evaluate.

  3. 03

    Build

    Implement selected infrastructure, pipelines and workload controls in your repositories and environment.

  4. 04

    Enable

    Validate the result with your team and hand over code, operating guidance and clear ownership.

Productized engineering packages

Nine clear ways to move forward.

Each package starts with a bounded problem and defines the work, deliverables and acceptance checks before implementation.

Cloud FoundationCloud Architecture ReviewWe review architecture, identity, networking and policy across your cloud estate. Findings are ranked by risk, impact and effort, with practical remediation options. You receive a decision-focused roadmap your team can use to plan the next changes.
Indicative timeline: 3–5 business daysView package →
 Cloud FoundationCloud Landing Zone AcceleratorWe establish the agreed tenant, subscription and network foundations with policy and identity controls. Reusable infrastructure code makes the setup reviewable and repeatable. Your team receives deployment guidance and a clear handover for ongoing ownership.
Indicative timeline: 2–4 weeksView package →
 Cloud FoundationCloud Modernization SprintWe select one workload and agree a secure target architecture and migration boundary. The sprint addresses priority changes in infrastructure, configuration and deployment. You leave with a cutover plan, validation steps and operational notes for the modernized workload.
Indicative timeline: 2–4 weeksView package →
 Platform EngineeringDevOps & IaC AssessmentWe examine infrastructure code, deployment workflows and team handoffs. A focused review identifies reliability, security and delivery bottlenecks using evidence from your repositories and pipelines. You receive prioritized recommendations with practical next steps for the teams involved.
Indicative timeline: 3–5 business daysView package →
 Platform EngineeringIaC & CI/CD AcceleratorWe turn a selected infrastructure pattern into reusable, version-controlled components. Automated checks and a controlled CI/CD workflow make changes easier to review and promote. Your team receives working pipeline examples and guidance for adapting them to other environments.
Indicative timeline: 2–3 weeksView package →
 Platform EngineeringKubernetes Platform SprintWe define a secure managed Kubernetes baseline for your chosen cloud and workload needs. The sprint configures core access, networking, policy and observability controls with repeatable deployment. Your team receives an onboarding path and operating guidance to support the first workload.
Indicative timeline: 3–5 weeksView package →
 Enterprise AIAI Readiness AssessmentWe assess priority use cases, data readiness, security and platform constraints with your stakeholders. The review surfaces dependencies, risks and operating requirements before implementation begins. You receive a ranked set of recommendations and a practical roadmap for the next decision.
Indicative timeline: 3–5 business daysView package →
 Enterprise AIEnterprise RAG PilotWe shape one retrieval use case around approved knowledge sources and user groups. A bounded pilot implements secure ingestion, retrieval and access controls, then evaluates answer quality against agreed questions. You receive measured findings and a recommendation for production readiness or further work.
Indicative timeline: 3–5 weeksView package →
 Enterprise AIProduction AI FoundationWe build shared controls for identity, network access, secrets, monitoring and model integration. Reusable infrastructure and delivery workflows support governed rollout across AI workloads. Your team receives operational guidance and ownership boundaries for maintaining the production foundation.
Indicative timeline: 4–8 weeksView package →

Timelines are initial planning ranges, not delivery commitments. Scope and fees are confirmed after discovery; cloud consumption, licenses and third-party services are separate.

Engineering examples

See how the layers connect in practice.

These published case studies describe representative implementation patterns, not named-client results.

Cloud FoundationA governed Azure foundationSet identity, policy, and network guardrails in a reusable Azure baseline. Give teams a consistent way to onboard workloads with clear ownership and controls.Read the pattern → Platform EngineeringInfrastructure delivery through CI/CDAutomate infrastructure delivery with reusable components and validated CI/CD workflows. Federated identity and controlled releases keep every change traceable.Read the pattern → Enterprise AISecure AI infrastructure and applicationsConnect governed data access, secure retrieval, and bounded tools to production operations. Shared controls help teams deploy AI applications with clear oversight.Read the pattern →

Designed for your environment

Start with the problem. Agree the scope. Hand over work your team can operate.

Typical deliverables include architecture decisions, infrastructure and pipeline code, validation guidance, runbooks and an operational handover, as appropriate to the agreed engagement.

Book an Architecture Review → Open the library ↗ Why choose Tongtech?