Everything a modern cloud company needs. Delivered by one senior team.

Well-Architected by default, not by review.

We design secure, resilient, serverless-first architectures on AWS and we write them down in code, not diagrams alone. For existing estates we run formal AWS Well-Architected Framework Reviews, remediate the high-risk findings and help you claim the AWS credits that come with them.

Typical engagement: 2 to 6 weeks for a design or review; longer builds run as sprints.

• Landing zones & multi-account strategyAWS Organizations, Control Tower, identity, guardrails and a network design that will not need redoing at scale.
• Serverless-first application architectureAPI Gateway, Lambda, Step Functions, EventBridge, DynamoDB and Aurora Serverless where they fit, containers where they fit better.
• Security & compliance baselinesEncryption, IAM least privilege, logging, GuardDuty and Security Hub mapped to SOC 2, ISO 27001 or regional requirements.
• Resilience & disaster recoveryMulti-AZ and multi-region designs with RTO and RPO targets you can test, and the cost of each option made explicit.
• Well-Architected Framework ReviewsStructured reviews across all six pillars, remediation plans and support with the AWS funding programs attached to them.

We scrutinize every line of your bill. Then we teach your team to.

Optimization is not a one-off project, so we do not sell it as one. We find and remove the waste first, then leave you with the metrics, guardrails and habits that keep it gone. Engagements are scoped against a savings target agreed up front. If we cannot see the savings, we say so before you commit.

Read our playbooks on AI/ML, Kubernetes and Karpenter cost optimization.

Typical engagement: 3 to 6 weeks for diagnosis and quick wins; optional monthly FinOps retainer.

• Cost diagnostic & savings planA line-by-line review of Cost and Usage Report data with quantified, prioritised actions and owners.
• Right-sizing & architecture-level savingsCompute, storage, databases, data transfer and observability. The expensive fixes are usually architectural, not instance sizes.
• Kubernetes & Karpenter economicsRequest right-sizing, bin packing, Spot and Graviton adoption and NodePool design on Amazon EKS.
• AI/ML & GenAI cost engineeringGPU utilisation, Spot training, inference right-sizing, Bedrock model tiering and token governance.
• Commitment strategySavings Plans and Reserved Instance portfolios modelled against real growth, bought after the baseline shrinks, not before.
• FinOps operating modelTagging, allocation, unit economics, budgets and anomaly alerts routed to the engineers who can act on them.

Move what should move. Modernize what should change. Retire the rest.

From data centers, colocation and other clouds to AWS, and from legacy VMs to containers and serverless. We plan in waves, automate the cutovers and keep the business running through every one of them. Where you qualify for the AWS Migration Acceleration Program, we help you use it.

Typical engagement: 4 to 8 weeks for assessment; migration waves sized to your risk appetite.

• Discovery, assessment & TCOInventory, dependency mapping, a business case in your CFO’s language and a wave plan with real dates.
• Migration executionAutomated, repeatable cutovers with AWS Application Migration Service, Database Migration Service and infrastructure as code.
• Database modernizationCommercial to open-source engines, Aurora, DynamoDB and the schema and application changes that come with them.
• Application modernizationContainers on EKS or ECS, serverless decomposition and API-first refactoring, prioritised by business value.
• Migration Acceleration Program supportAssessment, mobilise and migrate phases structured to meet AWS funding requirements.

Make the right path the easy path.

We build internal developer platforms that engineers actually want to use. Infrastructure as code, golden pipelines, GitOps and observability that answers questions instead of generating alerts. The goal is a team that ships daily and sleeps at night.

Typical engagement: 6 to 12 weeks for a platform foundation; embedded platform engineers on retainer.

• Infrastructure as codeTerraform or AWS CDK modules, tested and versioned, with a workflow that makes drift impossible to ignore.
• Kubernetes platform on Amazon EKSCluster design, Karpenter, ingress, service mesh where justified, policy and multi-tenancy that scales with the org.
• CI/CD & GitOpsGitHub Actions, GitLab or CodePipeline feeding Argo CD or Flux. Preview environments per pull request, progressive delivery.
• Observability & SRESLOs, error budgets, OpenTelemetry, dashboards and on-call design. Alerts that page for user impact only.
• Security in the pipelineImage scanning, secrets management, policy as code and supply-chain controls that do not slow developers down.

Machine learning that keeps working after the demo.

We take models from notebook to production on AWS and make sure the economics work at scale. Training at the right price, inference that scales down as well as up, and the MLOps around it so models keep earning their keep after launch.

Typical engagement: 2 to 4 week PoC, then sprint-based delivery.

• ML platform & MLOpsAmazon SageMaker or Kubernetes-based pipelines, feature stores, model registry, CI/CD for models and reproducible training.
• Training cost engineeringManaged Spot training with checkpointing, right-sized accelerators, mixed precision and AWS Trainium evaluations.
• Inference architectureReal-time, serverless, asynchronous and batch inference matched to traffic shape; Inferentia and Graviton where they win.
• Monitoring & retrainingData and model drift detection, quality metrics in production and automated retraining triggers.
• Data foundationsLakes, lakehouses and feature pipelines on S3, Glue, Athena and Redshift built for both analytics and ML.

Generative AI with evals, guardrails and a budget.

We build on Amazon Bedrock and the wider AWS AI stack: retrieval-augmented generation, agents, document intelligence and copilots that survive contact with real users. Every build ships with an evaluation harness and a token cost model, because quality per dollar is the metric that decides whether it stays in production.

Typical engagement: 2 to 4 week PoC; production hardening in sprints.

• Use-case discovery & feasibilityRank candidate use cases by value, risk and data readiness. Prove the best one in a fixed-scope sprint.
• RAG & knowledge systemsBedrock Knowledge Bases, OpenSearch or Aurora pgvector, chunking and re-ranking strategies tuned on your documents.
• Agents & workflow automationTool-using agents with Bedrock Agents or open frameworks, orchestrated with Step Functions and observable end to end.
• Evaluation & guardrailsGolden datasets, automated evals, Bedrock Guardrails, prompt injection defences and human review loops.
• Token economics & governanceModel tiering and routing, prompt caching, batch inference, provisioned throughput decisions and per-feature cost tracking.