Predictive Quality
Anticipate quality issues before they occur with ML-powered prediction models
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Data Engineering & AI in Production
Harness the power of artificial intelligence for predictive quality, anomaly detection, and process optimization. We build scalable ML pipelines tailored to your manufacturing needs.
When this is the problem: Your teams have production data, but no reliable path from signal to better quality or process decisions.
Business outcomes
Anomaly Detection
Automatically identify deviations and process instabilities in real-time
Process Optimization
Use AI to continuously optimize energy consumption and process parameters
Fast Implementation
Leverage our Factory Pilot framework for rapid deployment of AI solutions
How we engage
1
Frame a high-value use case
Select a measurable quality, energy or stability problem with operational ownership.
2
Make the data decision-ready
Create the data model, sensor mapping and quality controls needed to trust the signal.
3
Deploy with the operating team
Deploy the model into the daily workflow and measure its business impact.
What your team receives
- Prioritized AI use-case backlog
- Production-ready data model
- Measured pilot and scale-up decision
Relevant client proof
S
25%
Efficiency Gain
Swissbit: Industrial Engineering Excellence
Engagement year: 2013
Swissbit, a leading manufacturer of industrial-grade storage solutions, needed to optimize their production processes and improve operational efficien…
View Case Study →C
100%
Backlog Eliminated
Continental: TaskForce Microcontroller & OEE Improvements
Engagement year: 2016
One of Continental's major semiconductor vendors (Renesas) was severely affected by the series of earthquakes in Japan in April and August 2016. WIP-m…
View Case Study →Discuss the performance gap
Bring the constraint, escalation or growth target. We will identify the operating levers and the first practical next step.
Book a 30-minute operations assessment