Log management and application health monitoring practices are cumbersome and often still require significant human intervention to prevent inaccurate data and information. Existing technologies like Elasticsearch and Grafana offer opportunities to automate and improve these practices. This paper reports on a design solution aimed at enhancing log categorization, anomaly detection, and real-time application health reporting for CAPE Groep’s service application. The proposed solution leverages Elasticsearch’s Machine Learning capabilities and Grafana’s dynamic visualization tools, alongside a newly developed dashboard named Horus, to centralize log data and automate monitoring processes. Preliminary results indicate that the proposed solution significantly improves the accuracy and timeliness of health reports, reduces manual intervention, and provides comprehensive real-time insights into application performance. This paper outlines the requirements, architectural design, and phased implementation plan, demonstrating the potential to streamline operations, enhance service delivery, and support future more stringent scalability requirements.