Contovista’s bug reporting process for enrichment relied on multiple disconnected tools (Jira, Serena, Slack, and email), which led to incomplete reports, duplicated fixes, delays. The lack of standardization, automation, and transparency hindered resolution speed, traceability, and stakeholder satisfaction.

Using the Design Science Research approach, a literature review, and stakeholder interviews, we modelled current workflows with BPMN/sequence diagrams. Functional and nonfunctional requirements were defined using IEEE/Volere templates and prioritized using MoSCoW. AI feasibility was assessed for automated triage, and metadata capture.

The final concept proposes a single, standardized reporting channel with guided forms, automated metadata capture, and AI-assisted triage. This reduces cognitive load, detects duplicates, and improves prioritization. Expected benefits include reduced time to resolution, improved traceability, prevention of redundant fixes, and greater stakeholder satisfaction. While not implemented within the scope of this thesis, the design provides a blueprint and strategic roadmap for implementation, enabling Contovista to improve data quality, operational efficiency, and client trust in their enrichment services.