Engineering for systems that have to work in production
One team across cloud, software, AI and data. The same engineers, standards and AWS platform behind our client work and the ventures we support.
Proven in production
The ventures we build and support run on the same engineering core we use for clients. Patterns that reach you have already carried real traffic.
Every build inherits infrastructure as code, an automated pipeline, observability before launch, and a Well-Architected review before anything ships.
Five disciplines, one engine
Pick a discipline to see what we actually do inside it. Each one plugs into the same engineering core, so nothing is built in isolation.
Intelligence, built in.
AI designed into the product, not around it — agentic workflows, generative interfaces, and automation that carry real operational load.
AI agents
Tool-using agents that plan, execute and act inside real workflows.
Generative systems
Generative features for text, structured output and content.
Intelligent automation
Document, decision and back-office processes automated end to end.
Model integration
Foundation and hosted models behind clean interfaces.
Inference
Models served with sensible latency and cost trade-offs.
Evaluation
Measurable model behaviour, so changes are testable.
The standards every build inherits
Not per-project decisions. Quality does not depend on who staffed the team.
Well-Architected first
Every design reviewed against the six AWS pillars before build.
Infrastructure as code
No console-clicked environments — CloudFormation or Terraform.
Automated delivery
Build, test, scan and deploy on a pipeline from week one.
Observable by default
Metrics, logs and traces wired before the first user lands.
FinOps built in
Tagging, allocation and commitment strategy from day one.
Handover-ready
Runbooks and diagrams maintained as deliverables.
Engineering on AWS
The services our architectures draw on, grouped by the role they play.
Compute
Data
AI
Integration
Security & ops
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