What challenges did Workerbase face before AI implementation?

Manufacturing workflows were inefficient and disconnected, relying on paper-based processes and siloed legacy systems leading to delays, errors, and poor visibility.

What was the impact of the AI solution?

Workflow deployment times were reduced dramatically from months to hours, enabling agile operational changes. Machine downtime was minimized, leading to increased Overall Equipment Efficiency (OEE). Productivity was enhanced through AI-generated multilingual instructions and seamless integration of legacy IT systems. Operational efficiencies improved for customers including Siemens and Porsche, with reduced error rates and faster task resolution.

Source

GCPExpandedProductionEvidence: Medium55/100

Workerbase: Empowering Shop Floor Workers with AI in Manufacturing

Use case typePredictive maintenanceUpdated Jun 13, 2026

Workerbase developed an AI-powered platform to digitize and integrate disconnected manufacturing workflows, traditionally reliant on paper and siloed legacy systems like SAP and Siemens software. The platform delivers real-time, context-aware actionable insights directly to shop floor workers, including predictive maintenance alerts and multilingual task instructions. The use of low-code app development tools enables rapid deployment of customized workflows, reducing deployment times from months to hours. Integration of Google Cloud technologies including Vertex AI, Google Kubernetes Engine, BigQuery, and Cloud Storage underpins the intelligent, scalable platform. Workerbase's platform has demonstrated improved Overall Equipment Efficiency (OEE), reduced machine downtime, enhanced productivity, and greater operational efficiency at major manufacturers such as Siemens and Porsche.

Organization: Workerbase
Industry: Manufacturing
Location: Germany
Published: June 2024

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Workerbase, Siemens, Porsche
Provider: GCP
Maturity: Production
Linked source: Google Cloud Customer Stories

Workerbase's platform has demonstrated improved Overall Equipment Efficiency (OEE), reduced machine downtime, enhanced productivity, and greater operational efficiency at major manufacturers such as Siemens and Porsche

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 2 of 2

1Workflow Automation
2Predictive Maintenance

Workerbase built an AI-powered platform using Google Cloud technologies like Vertex AI for AI-driven insights and Google Kubernetes Engine for scalable low-code workflow delivery.
The platform integrates legacy systems, enabling real-time data flow and AI-powered context-specific instructions for operators.
Low-code tools enable shop floor supervisors to rapidly build and deploy tailored workflow apps.
AI models support troubleshooting and predictive maintenance, providing real-time alerts and step-by-step instructions in appropriate languages for on-floor workers.

Technologies

Vertex AI Google Kubernetes Engine BigQuery Cloud Storage

Workflow deployment times were reduced dramatically from months to hours, enabling agile operational changes.
Machine downtime was minimized, leading to increased Overall Equipment Efficiency (OEE).
Productivity was enhanced through AI-generated multilingual instructions and seamless integration of legacy IT systems.
Operational efficiencies improved for customers including Siemens and Porsche, with reduced error rates and faster task resolution.

Architecture

The solution architecture combines Vertex AI's advanced AI and machine learning capabilities with Google Kubernetes Engine for app deployment and scaling, BigQuery for analytics, and Cloud Storage for data persistence. The platform integrates legacy manufacturing systems to provide real-time AI-driven workflows and predictive maintenance alerts directly to workers on the shop floor.

Sources & evidence1

Evidence: Medium55/100Evidence strength