# How AI Fleet Management Architecture Transformed Our Legacy Systems

This talk explains how a fleet management and telematics company used AI to modernize two aging, overlapping platforms and speed up product delivery. The effort began out of necessity: legacy .NET systems, duplicated architecture, and slow innovation pushed the team to build a prototype that could assess the current estate, map it to a target architecture called Vanguard, identify gaps, and even update Jira work automatically. The speaker argues that context matters more than prompts, so the company created a “vault” and audit layer to store knowledge, track every action, and build trust. Governance became central rather than optional, with three core rules: AI gets no production or direct data access, nothing governs itself, and every action must be provable. Results included building work in 60 hours that vendors estimated at 15 weeks and $170,000, and reducing one internal AI workflow from $13,000 a day to about $20. The biggest challenge was not models, but people, ownership, adoption, and changing operating habits fast enough to keep up.