Most AI projects start with a simple question: “What can AI do?”
My project started with a different question:
“What problems do maintenance technicians actually face every day?”
As a maintenance supervisor, I spend more time troubleshooting equipment than writing code. When an HVAC unit fails, a dryer stops heating, or a refrigerator quits cooling, the challenge isn’t finding information. The challenge is finding the right information quickly.
That experience led me to build Fix-It Fast AI.
The goal wasn’t to create another chatbot. The goal was to create a tool that could help identify equipment, understand symptoms, and provide practical troubleshooting guidance.
One of the biggest lessons I learned is that real-world problems rarely fit into neat categories.
A technician might upload a blurry equipment label.
A homeowner might describe symptoms incorrectly.
A model number might be partially faded or covered in dirt.
Traditional software expects clean inputs. Real life doesn’t.
Because of that, I spent significant time improving image processing, OCR recognition, and error handling rather than focusing only on AI prompts.
Another lesson was the importance of domain knowledge.
Large language models are impressive, but they don’t automatically understand the thousands of details involved in appliance repair, HVAC systems, electrical troubleshooting, and maintenance operations.
The quality of answers depends heavily on the quality of information available to the system.
That’s why building a repair knowledge base became just as important as building AI features.
The project continues to evolve, but the biggest takeaway remains simple:
The best software doesn’t start with technology.
It starts with understanding the people who will use it and the problems they need solved.
For me, that meant bringing years of maintenance experience into the software development process and building something designed for real technicians working in real conditions.
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