AWS, for workloads that need to scale globally.
AWS is the world's most widely adopted cloud platform, and usually our default when a product needs to scale fast, serve users globally, or lean on the broadest managed-service catalogue available. We use it for everything from legacy migrations to fully cloud-native, AI-driven applications.
Where AWS earns its place in the stack.
Cloud migration, minimal downtime
Moving legacy systems onto AWS without the extended outage window migrations usually threaten.
Data engineering at scale
Redshift, Glue and Lake Formation build the pipelines and warehousing behind products that outgrow a single database.
AI and machine learning
SageMaker turns a data platform into something that can predict and automate, not just report.
Cloud-native application development
EC2, Lambda and S3 give us the primitives to build applications that scale globally from day one.
Unmatched breadth, when a project needs it.
AWS is usually our first choice when a project needs to scale globally or lean on the broadest set of managed services available. It's mature enough that whatever edge case comes up, someone else has usually already solved it.
It's also where a lot of our data engineering and AI work lives. Redshift, Glue and SageMaker cover the pipeline-to-model journey without forcing us to stitch together a dozen separate tools.
The broadest service catalogue available
Whatever unusual problem comes up, there's a strong chance AWS already has a managed service for it.
Built to scale globally
AWS's global infrastructure means a product can serve users on the other side of the world without us re-architecting it.
Enterprise-grade security by default
Fine-grained IAM and a mature compliance footprint mean the security model is solid before we've deployed anything.
A genuine head start on AI
SageMaker and the wider AI service catalogue mean we're rarely building machine learning infrastructure from scratch.
Scaling fast, or need to? AWS is usually the right call.
Book a 30-minute working session with a senior engineer — a real conversation about your infrastructure, not a sales call.
What an AWS build usually includes.
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Read moreThe stack we ship on.
Pragmatic, mostly boring, and chosen because it works in production — not because it's on the front page of Hacker News.
Have an outcome in mind?
We'll help you
ship it.
- You're building a data product and need a team that can deliver.
- You want to get AI-ready — pragmatically, not theoretically.
- Your reporting is a mess and you need a real platform underneath it.