A Continuous Cycle of Improvement
makes our Approach Unique.

A Continuous Cycle of Improvement
makes our Approach Unique.

We work closely with our customers to configure our platform to enable a continuous cycle of improvement.

Our first step is to work with our customers to identify all applicable data. We then deploy Eigen hardware allowing for real-time continuous data collection and ongoing visibility into their manufacturing process. We help them uncover insights they have not realized before.

We then develop algorithms to begin detecting impacts to cost of quality in real-time. This allows our customers to fix problems as they occur.

Along with real-time process monitoring, we provide on advanced data analytics application that helps all manufacturing stakeholders drive towards true process optimization. Our software provides access to quality analytics on a part-by-part and machine-by-machine basis, at any given time, on multiple devices including mobile.

Over time, our solution begins to predict outcomes before they happen. This unlocks the true value of Artificial Intelligence and allows manufacturers to leave the frustration of problem solving behind them.

Examples of Current Customer Applications

Plastics Joining Machine Supplier

The Problem: Providing global manufacturers of high value automotive parts with ongoing visibility of the quality issues within their plastics welding processes.

Eigen Solution: Real-time correlation of machine data and product output.

Automotive Parts Supplier

Problem: Quality control within plastics welding process for automotive lift gates.

Eigen Solution: Correlating machine data and product output to determine optimal machine performance parameters.

Injection Molding Parts Supplier

The Problem: Defects and variability within the Injection Molding process.

Eigen Solution: Applying real-time machine learning using thermographic and in-process cycle data to drive process optimization.

Automotive OEM Factory

The Problem: Quality control within the die casting process on critical structural parts. Defects are only visible late in post processing which has a significant cost of quality.

Eigen Solution: Applying real-time machine learning using thermographic and in-process cycle data to drive process optimization.

Manufacturer of Sheet Metal Used in Automotive Steel Bumpers

Problem: Imperceptible defects that only become visible post-processing.

Eigen Solution: Real-time thermographic inspection using a customized machine learning algorithm to identify defects within their process.

Automotive OEM Factory

The Problem: Quality control within the Adhesive Dispense process on windshield glue-beads.

Eigen Solution: Real-time thermographic inspection of the dispensing process.

We work closely with our customers to configure our platform to enable a continuous cycle of improvement.

Our first step is to work with our customers to identify all applicable data. We then deploy Eigen hardware allowing for real-time continuous data collection and ongoing visibility into their manufacturing process. We help them uncover insights they have not realized before.

We then develop algorithms to begin detecting impacts to cost of quality in real-time. This allows our customers to fix problems as they occur.

Along with real-time process monitoring, we provide on advanced data analytics application that helps all manufacturing stakeholders drive towards true process optimization. Our software provides access to quality analytics on a part-by-part and machine-by-machine basis, at any given time, on multiple devices including mobile.

Over time, our solution begins to predict outcomes before they happen. This unlocks the true value of Artificial Intelligence and allows manufacturers to leave the frustration of problem solving behind them.

Examples of Current Customer Applications

Plastics Joining Machine Supplier

The Problem: Providing global manufacturers of high value automotive parts with ongoing visibility of the quality issues within their plastics welding processes.

Eigen Solution: Real-time correlation of machine data and product output.

Automotive Parts Supplier

Problem: Quality control within plastics welding process for automotive lift gates.

Eigen Solution: Correlating machine data and product output to determine optimal machine performance parameters.

Injection Molding Parts Supplier

The Problem: Defects and variability within the Injection Molding process.

Eigen Solution: Applying real-time machine learning using thermographic and in-process cycle data to drive process optimization.

Automotive OEM Factory

The Problem: Quality control within the die casting process on critical structural parts. Defects are only visible late in post processing which has a significant cost of quality.

Eigen Solution: Applying real-time machine learning using thermographic and in-process cycle data to drive process optimization.

Manufacturer of Sheet Metal Used in Automotive Steel Bumpers

Problem: Imperceptible defects that only become visible post-processing.

Eigen Solution: Real-time thermographic inspection using a customized machine learning algorithm to identify defects within their process.

Automotive OEM Factory

The Problem: Quality control within the Adhesive Dispense process on windshield glue-beads.

Eigen Solution: Real-time thermographic inspection of the dispensing process.