Note: This is a documentation-only case study. The original tool was developed for internal operational use. Source code, internal data, customer information, screenshots, and implementation-specific details are not included.
iBuddy is an internal support analytics workflow designed to help support teams extract, organize, and analyze customer support conversations more efficiently. The goal was to reduce manual ticket review, improve visibility into recurring issues, and support better operational reporting.
Support teams often need to understand what customers are reporting across large volumes of conversations. Manually reviewing tickets can be slow, inconsistent, and difficult to scale. Teams also need repeatable ways to identify recurring issue patterns, product friction, documentation gaps, and support trends.
The workflow was designed to let support users select a date range, extract relevant support conversations, organize the results into spreadsheet-friendly formats, and prepare the data for review and analysis. A recurring reporting workflow was also created to generate periodic insight files for different support areas.
- Extracts support conversations based on selected date parameters
- Organizes exported data into spreadsheet-friendly formats
- Supports recurring trend analysis across support areas
- Helps identify repeated customer issues and documentation gaps
- Reduces manual review effort for support and technical support teams
- Enables AI-assisted summarization and issue analysis workflows
- Support automation
- Data extraction and organization
- Ticket analysis
- Workflow design
- Python-based automation
- Reporting support
- Documentation and repeatable process design
- Operational trend analysis
The workflow improved the ability to review large volumes of support conversations and identify recurring themes more efficiently. It helped turn support data into clearer operational insight and made it easier to support documentation, reporting, and escalation workflows.
This project reflects my interest in support engineering, developer support, technical operations, and building tools that reduce repeated manual investigation. It is especially relevant to roles focused on support tooling, documentation, customer advocacy, and operational improvement.