Customer Story
AI Risk Mitigation in Power Infrastructure
In this case study, LatticeFlow AI partnered with Axpo, Switzerland's largest renewable energy provider, to assess and mitigate AI risk by detecting hidden model blind spots, improving performance with minimal relabeling, and establishing a repeatable validation workflow.
As a result, both teams developed a unique blueprint for accelerating AI innovation, without compromising on safety and reliability.
+16%
Increased Precision
+9%
Increased Recall
<50
Target Labeled Examples
See how LatticeFlow AI helped Axpo improve AI performance and reliability to scale AI innovation.
In a domain like energy infrastructure, a reliable AI is fundamental. LatticeFlow AI provides us the visibility and control to ensure our computer vision models are both high-performing and secure in daily business. With this certainty, we can detect and fix issues fast and scale AI safely across operations. LatticeFlow AI is a key enabler for accelerating AI innovation without compromising on trust in our AI models.
- Kevin Geiger
Project Engineer Asset Analysis, Axpo
Challenge
For AI monitoring critical infrastructure, model performance is directly connected to operational reliability. Yet failures can remain hidden in real-world data and become increasingly difficult to identify at scale.
Axpo needed a more efficient way to detect these blind spots and maintain confidence in AI performance without adding significant manual effort.
Solution
LatticeFlow AI gave Axpo deeper visibility into where its computer vision models were underperforming and helped the team focus mitigation efforts where they would have the greatest impact.
The result was a more systematic approach to identifying and addressing AI performance risks in daily operations.
Results
Axpo achieved measurable improvements in both precision and recall while requiring fewer than 50 targeted labeled examples.
Beyond the immediate performance gains, the project demonstrated how Axpo can assess and improve AI reliability more efficiently as it scales AI across critical infrastructure use cases.
Read the full customer story to discover how Axpo identified hidden model failures, targeted the right mitigations, and improved AI performance.
Challenge
For AI monitoring critical infrastructure, model performance is directly connected to operational reliability. Yet failures can remain hidden in real-world data and become increasingly difficult to identify at scale.
Axpo needed a more efficient way to detect these blind spots and maintain confidence in AI performance without adding significant manual effort.
Solution
LatticeFlow AI gave Axpo deeper visibility into where its computer vision models were underperforming and helped the team focus mitigation efforts where they would have the greatest impact.
The result was a more systematic approach to identifying and addressing AI performance risks in daily operations.
Results
Axpo achieved measurable improvements in both precision and recall while requiring fewer than 50 targeted labeled examples.
Beyond the immediate performance gains, the project demonstrated how Axpo can assess and improve AI reliability more efficiently as it scales AI across critical infrastructure use cases.
Read the full customer story to discover how Axpo identified hidden model failures, targeted the right mitigations, and improved AI performance.
See How Axpo Put
AI Risk Control Into Practice
Turn AI Risk into AI Advantage
See the LatticeFlow AI Platform in Action.