Jeremy Neely
Text Jeremy
Web Apps & Dashboards · Production
Fleet Insights
Monthly repair reports that show each branch which trucks cost too much.
Built for: A regional truck dealer and service company that runs fleet maintenance for a commercial customer with about 85 branches
The problem
Every branch of a large commercial fleet sends in repair and maintenance invoices each month. Branch managers had no simple way to tell whether their spend was normal for that equipment, which units drove the cost, or when a truck should be replaced instead of repaired. The old process ran on spreadsheets.
What I built
I built a four-stage Python pipeline that compares every unit with peers of the same type and age and writes one self-contained report per branch, per district and for management. Around it are a Report Builder desktop app for the program staff, a Claude review of every report before it goes out, and a guarded workflow that lets non-developers ask for report changes in plain English.
How it works
- Pull a month of repair and maintenance records through a secure MCP connection.
- Match each unit with similar units by year, make, model and type.
- Rank the units and branches that spend most above their peers, with keep or replace verdicts.
- Have Claude check every report against a written checklist.
- Publish one interactive HTML report per branch and district.
Built with
- Python
- pandas
- PySide6
- Claude Agent SDK
- MCP
- HTML/CSS/JavaScript
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Desktop Apps
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