What it is for
Dashboards, agents and automations are only as good as the data under them. When that layer is a collection of one-off pipelines, every new source is a project and nobody fully trusts the numbers.
What we build
- Data engineering. Ingestion, modelling and transformation that is version-controlled and tested: dbt, SQL and Python on a warehouse such as Amazon Redshift.
- Cloud engineering. Infrastructure as code (AWS CDK), containerised processing on Kubernetes, and orchestration with tools such as Argo Workflows.
- System integration. Standard entry points (API, SFTP, JDBC), so sources such as CRM, ERP and research databases arrive the same way.
- Digital transformation. The technical foundation a wider change depends on, built so it can be extended rather than replaced.
How we approach scale
Scale is not just about bigger servers; it is about better abstractions. We prefer one framework, configured per source, over many pipelines written by hand.
Proof
The enterprise data infrastructure case study: a metadata-driven ingestion framework for a global pharmaceutical company, managing 300+ sources and 70+ TB, that reduced source onboarding from weeks to days. The architecture is explained in the guide Data ingestion at scale.
