A Global Semiconductor Manufacturing Leader
Rebuilds a Legacy Data Platform into a
Governed, AI-Ready Foundation
100%
Deployment Traceability
99%+
SLA Adherence on Data Refreshes
90%
Reduction in Pipeline Deployment Time
Semiconductors
Company Profile
- ✓Global leader in high-precision device manufacturing
- ✓Multi-site fabrication network serving worldwide demand
- ✓High-volume production held to nanoscale tolerances
The Challenge
The Solution
The Full Context
The enterprise was not short of data. It was short of a dependable path from source to decision. Each new system that needed onboarding got its own hand-built ETL pipeline, so the platform grew as a collection of one-off integrations rather than a single system. Development cycles stretched, and maintenance load compounded with every addition.
Operations sat in the same position. Scheduling was spread across mechanisms, deployments were manual, and no central monitoring existed, so failures could pass unnoticed until someone downstream questioned a number. Quality issues surfaced late, and because historical values were overwritten rather than versioned, tracing what a figure looked like at a given point in time was often not possible.
The effect on the business was a dependency. Teams could not see what data existed, so any question became a request to engineering, and the workarounds turned into ungoverned extracts sitting outside the platform.
AuxoAI ran a modernisation assessment and then built against it: a standardised ingestion layer, a governed architecture with lineage, and a discovery layer that lets business teams reach the data themselves. The last piece is a conversational intelligence application that answers questions over structured data in natural language and returns summarised data, charts and insights.
The Approach
AuxoAI delivered the build in three phases, establishing governance before extending self-service access to business teams.
Standardised How Data Arrives
A template-based ETL framework replaced bespoke pipelines with configuration-driven ingestion, so onboarding a new source became a matter of configuration rather than a development project. Onboarding time moved from weeks to days.
Made the Platform Governable
Automated CI/CD and Airflow orchestration brought version-controlled deployments, centralised scheduling and operational visibility. A medallion architecture with Bronze, Silver and Gold layers established lineage and auditability across every dataset.
Opened Access to the Business
A unified data catalog made enterprise datasets discoverable under governance, and a conversational GenAI application let users query structured data in natural language and receive summarised data, charts and insights.
The Impact
The platform delivered 100% deployment traceability, 99%+ SLA adherence on data refreshes, and a 90% reduction in pipeline deployment time.Deployment time fell by 90%, which changed what the data team could take on. Work that had been queued behind pipeline effort became work that could be scheduled, and new sources stopped competing with maintenance for the same engineering hours.
Reliability followed the governance. With centralised orchestration and monitoring in place, refreshes hold above 99% SLA adherence, and full deployment traceability means any change to the platform can be attributed and reviewed rather than reconstructed.
The quieter gain is trust. Lineage through the medallion layers makes a number explainable back to its source, and the catalog with conversational access means business teams reach that number directly. Requests that once required an engineer now resolve in the platform, and the ungoverned extracts that filled the gap have less reason to exist.
Project Highlights
Solution
Template-based, configuration-driven ETL framework
Automated CI/CD with Airflow orchestration
Governed medallion architecture with full lineage
Unified data catalog for self-service discovery
Conversational GenAI application over structured data