Ford Quality Agent
I designed an internal AI agent that helps Ford quality engineers investigate recurring vehicle issues. It brings warranty, vehicle, supplier, and plant repair data into one conversation so engineers can identify problems and decide what to investigate next. The agent contributed to approximately $55M in warranty and recall savings.
Engineers now catch a recurring issue before the vehicle leaves the plant. The data sits in one place, so cross-functional teams pull what they need instead of booking a meeting to ask for it.
Quality engineers had to gather data from several systems before they could decide which issues to investigate. Claims, vehicles, suppliers, and plant repairs each lived in a different system.
The expensive decisions waited on that data: which vehicle line to focus on, which supplier to charge back, which vehicle function to hand to which plant. Each one meant someone assembled the numbers by hand, then held a meeting to pass them on.
I started by interviewing quality engineers about where their time went and what got in the way. What they told me shaped the product. The Quality Agent puts all of that data behind one conversation. An engineer asks the way they would ask a colleague, and the answer comes back as a sentence, a chart, or a table.
I built it as a working HTML prototype, tested it with engineers, and revised it with them before handing it to the build team. Engineers were going to act on what the tool told them, so I made every state show its work.
I used Figma’s MCP server to read Ford’s design guidelines and built reusable HTML and CSS components for the prototype. This kept the screens consistent with Ford’s design system and gave the development team components to work from.
Launch was not the end of the work. The agent is live, and it contributes heavily to Ford's quality goals and to the warranty and recall savings behind them. I watch how engineers use it in Contentsquare, the product analytics tool. What I see there is what I change next.
All data shown is sample data from the visual prototype.
Grouped starter prompts give engineers a starting point for common tasks.
Packaged reports are slash commands. The agent asks for the one thing it cannot assume, then generates the rest.
Progress updates appear in the conversation and header while the report is generated.
Engineers can export each table to Excel or capture it as an image.
A trend question gets a chart with the reading written above it. A single-fact lookup gets one sentence.
