Capabilities · Technology · Cloud Infrastructure
A cloud engineering practice that builds cloud-native warehouse infrastructure, designs platform foundations, engineers production deployment for AI and data systems, and ships cloud migration and modernisation. Multi-cloud across Azure, AWS, and GCP.
02 · What we build
Five capabilities that span how cloud actually gets built in production — from data warehouse engineering to platform foundations to multi-cloud deployment and cloud transformation.
Building production data warehouses on cloud-native architecture — using Airflow, Azure Data Factory, AWS Glue, GCP Cloud Composer and Dataflow, and Snowflake on cloud. Schema design, pipeline orchestration, performance tuning, and cost engineering at design time — the engineering work that turns a cloud account into a data platform.
Production work across Azure (ADF, Synapse, Azure DevOps), AWS (Glue, Redshift, Lambda, EKS, CodePipeline), and GCP (BigQuery, Cloud Composer, Dataflow, GKE, Cloud Build). Cloud-neutral posture; the cloud follows the workload, the client's existing stack, and the regulatory constraints — not the other way around.
The infrastructure foundations production systems run on — Terraform, Kubernetes, Docker, CI/CD pipelines, infrastructure-as-code. Engineered for repeatability, observability, and operational handoff to the client's team after delivery.
Designing the cloud architecture for production AI models, vision pipelines, agentic workflows, and data systems. The engineering work that decides the deployment surface, the operational posture, the scaling pattern, and the cost profile — across managed cloud, on-customer infrastructure, sovereign regions, hybrid, and edge.
Cloud migration, modernisation, and cross-cloud work — legacy lift-and-shift, on-prem-to-cloud migrations, cross-hyperscaler modernisation. Designed for the target platform's native services rather than ported as-is, so the workload genuinely benefits from being in cloud.
03 · How we think about it
Cloud selection is too often a procurement decision made before the workload is understood. We choose cloud based on what's actually running on it — the data gravity, the existing stack, the regulatory posture, the team that operates it. Cloud-neutral as posture; opinionated about which platforms are genuinely good at which workloads.
Cloud cost optimisation isn't a separate FinOps engagement bolted on later. It's an architecture decision made at design time. We engineer cost into every workload we ship — right-sizing, autoscaling posture, storage tiering, query patterns. Optimisation done at design is significantly cheaper than optimisation done at audit.
Most cloud failures are systems that were ported to cloud without being engineered for it. Cloud-native means architecture that uses the platform's strengths — managed services, autoscaling, serverless where it fits, native orchestration — not lift-and-shift with cloud branding. We build cloud-native from the start, and we modernise legacy systems so they earn the cloud they're on.
04 · Tools and ecosystem
The platforms, services, and tools we ship cloud systems with — named honestly across Azure, AWS, and GCP, with our strategic partnerships shown separately.
Multi-cloud delivery. The cloud follows the workload, not the other way around.
Data pipeline orchestration, infrastructure-as-code, container orchestration, and CI/CD — named across the clouds we ship in.
The surface chosen per engagement — based on data residency, latency, IP, and operational requirements.
05 · FAQ
Four things we get asked early in cloud conversations. The honest answers, so you can decide whether a working session is worth your time.
We start from the workload, not the cloud. Azure fits well when ADF, Synapse, or Microsoft-ecosystem integration is already part of the stack. GCP fits well for data-heavy workloads where BigQuery and Dataflow are a natural fit. AWS works for broad ecosystem needs across infrastructure, with Glue, Redshift, Lambda, and EKS as common building blocks. The right answer depends on your existing stack, your team, your workload mix, and your regulatory posture — we'll walk through the decision in the first working session.
It depends on the workload. Lift-and-shift is the right answer when the existing system is stable, modest in scale, and the migration is mostly about getting off legacy infrastructure. Re-architecting matters when the workload can genuinely benefit from cloud-native services — managed scaling, native orchestration, serverless components — and the engineering investment pays back in operating cost or velocity. We'll walk through the trade-off honestly for your specific systems rather than defaulting to either extreme.
Yes. We've shipped to on-customer infrastructure, sovereign cloud regions, hybrid setups, and edge deployments. For regulated industries, IP-sensitive workloads, or data residency requirements, "cloud" isn't always the managed-public-cloud answer. We design the deployment surface deliberately based on the engagement's constraints.
Cost is engineered into the architecture from day one. Right-sizing, autoscaling, storage tiering, and query patterns are decided at design time, not optimised at audit time later. We're not a dedicated FinOps consultancy, but we don't ship workloads that are expensive by accident. For the standalone cloud transformation work we've shipped, cost reduction has often been a primary driver of the engagement.
Most cloud decisions are made before the workload is understood. We design cloud around what's actually running on it.
07 · Ready when you are
A working session with a senior cloud architect — 30-45 minutes, focused on your workload, your existing stack, your deployment constraints. No commitment. We leave you with useful thinking either way.
Related technology capabilities
LLM integration, SLMs, fine-tuning open-source, predictive ML, and the engineering layer that makes models reliable in production.
TechnologyModern data foundations on Snowflake, Databricks, and dbt — the ground layer most vision and AI work runs on.
TechnologyVision systems for operational intelligence — Visbi for multi-location retail, plus object detection, OCR, edge inference, and video analytics.
TechnologyNewAI agents that take action in client systems — operations triage, document orchestration, cross-system reporting. Three production patterns shipped.
Founded 2020 · AI & ML engagements delivered across North America, Australia, and India · Partnerships and methodology details on About
