Before we built agents, we built the data they think with.

Platform engineering, analytics and data science are where Astura began — and why our AI works in production, not just in demos.

You cannot bolt intelligence onto data you don't trust. Governed pipelines, clean lineage and a modern platform are the substrate every model, copilot and agent depends on — so we treat data engineering as the root capability, not a warm-up act.

What we build

Five foundations, engineered to production standard.

Data Platform Modernisation

Legacy estate to lakehouse transitions with transition-state strategy, cost calculators and zero-surprise cutovers.

Data Engineering & Pipelines

Batch, streaming and CDC — SLAs engineered, not hoped for.

Analytics & BI Modernisation

Semantic layers, self-service and executive dashboards that actually get used.

Data Science & ML Engineering

Forecasting, risk and optimisation models with explainability (SHAP) as standard; MLOps to keep them honest.

Data Governance & Quality

Cataloguing, lineage, access control and quality scorecards — the substrate AI governance depends on.

Modernise without the risk

Every migration ships with a transition-state architecture that keeps downstream consumers live.

Assess my estate

Modernisation spotlight

A governed lakehouse migration — with 400+ consumers kept live.

A major insurer's move from a legacy on-prem data platform to a governed lakehouse, sequenced by a transition-state architecture so nothing downstream broke.

Before

Legacy on-prem platform

Batch ETL (nightly)

400+ downstream consumers

Transition-state architecture

After

Governed lakehouse

Streaming + CDC · Unity-class catalog

Same 400+ consumers · zero downtime

Databricks Snowflake Azure AWS GCP Cloudera Kafka dbt Power BI · Tableau

We certify in ecosystems. We commit to outcomes.