Logistics & Manufacturing
Get visibility into routes and costs around the world. Ensure that AI is continuously delivering an advantage to your business by monitoring for model health and performance.
Real-time optimization of delivery routes powered by AI is a game changer for carriers and their customers. But when these AI models suffer from failures - poor performance in certain geographies or customer segments - it’s turns into a real-time disaster.
Huge competetive advantage can be derived from AI-driven cost optimizations. However, data that powers these models comes from unreliable data providers and bad quality data results in incorrect cost predictions which can result in large financial loses if they go unnoticed.
AI models improve customer expereince by optimizating for cost, route, and delivery time. However, if left unmonitored, certain customer segments can be hurt by poor model performance.
Monitor data and AI applications across complex, heterogeneous platforms
WhyLabs provides over 40 out of the box integrations that make it easy to enable monitoring in any infrastructure
Monitor every application and decision at global scale
Unify monitoring across hybrid cloud, in-house, and third-party systems
Get real-time insights into all AI-powered decisions
Catch and fix data and model issues before they impact the business
As logistics and manufacturing companies increasingly rely on AI to automate operations and optimize processes, their IT footprint becomes more dynamic and opaque. Traditional monitoring solutions do not provide visibility into AI-powered applications. WhyLabs enables these organizations to gain real-time ingiths into the health and performance of AI-powered applications, bringing together health signals about data quality, model health, and model performance in a single control panel.
Processes in logistics and manufacturing often rely on heterogeneous platforms, spanning on-premises and hybrid cloud infrastructure. WhyLabs provides simple, cost-effective, and extensible instrumentation which enables monitoring for data quality across any type of infrastcuture operated by the organization. Establishing a unified monitoring approach across all data workflows helps teams catch data quality issues at their inception and prevent them from impacting model predictions, thus insuring consistently high model model performance.
"WhyLabs’ monitors are detecting issues, such as predicting wrong values, that we might have never known about. The downstream impact of catching these issues is that we’re able to operate our business more successfully"
— Calvin Linford
Sr. Software Engineer, Airspace