Drive Peak Performance in Every Link of Your Supply Chain

Successful adoption is something we plan for from day one. XAI platform is designed with a human-in-the-loop approach, enabling users to query the system to understand how a recommendation was created: what data was used, what boundaries are there, and point to which features were driving the recommendation. This transparency allows users to combine data-driven insights with their own domain expertise and intuition. By involving users early in the design process, users understand how the XAI model aligns with their real-world understanding. Tracking ROI of the system reinforces confidence in the collaborative decision-making model.

Yes. Our XAI platform integrates seamlessly with existing enterprise and inventory management systems through secure APIs and data connectors. It supports both cloud and on-prem environments, ingesting structured and unstructured data to deliver explainable, AI-driven insights within your current tools. This approach enhances existing workflows, minimizes disruption, and builds on your established infrastructure rather than replacing it.

Absolutely! By analyzing your current operations, distribution network, and forecasting demand, Mined XAI can recommend locations that will reduce delivery time and ensure that products are as close to the end customer as possible. Depending on your individual goals for the new distribution center, we can provide recommendations backed up with the data to support them.

We have customers where this describes their supply chains as well, and our software carefully examines these factors and others to provide the most accurate forecast possible. External data sets such as weather can be easily added to our models to provide the most comprehensive view of your demand.

Yes. Mined XAI’s deep learning methods were initially created so that they have full transparency, inspectability, and can create easily explained solutions that are very complex. This explainability can be used to fully justify implemented models that meet regulatory compliance when needed.