๐ง๐ต๐ฒ ๐๐ถ๐ด๐ด๐ฒ๐๐ ๐ ๐ถ๐๐๐ฎ๐ธ๐ฒ ๐ ๐ ๐ฎ๐ฑ๐ฒ ๐ช๐ต๐ฒ๐ป ๐๐๐ถ๐น๐ฑ๐ถ๐ป๐ด ๐ฎ๐ป ๐๐ ๐ง๐ฟ๐ผ๐๐ฏ๐น๐ฒ๐๐ต๐ผ๐ผ๐๐ถ๐ป๐ด ๐ง๐ผ๐ผ๐น
I built an AI troubleshooting tool. I thought the AI was the hardest part. I was wrong.
The real challenge was bad data. Users do not provide clean information.
- Labels are dirty.
- Photos are blurry.
- Model numbers are missing.
- Users lack technical terms.
AI needs data to work. I changed my focus. I spent more time on data collection and image processing. The system must understand the problem before it answers.
Users do not care about the model. They care about the result.
- Homeowners want a fix.
- Technicians want causes.
- Managers want less downtime.
Reliability beats sophistication. A simple, correct answer wins. Complex answers sound good but offer no value.
Winning products combine AI with domain knowledge and quality data. Solving real problems requires more than AI. You need to understand people and processes.