For 50 years, computers processed text and numbers. If you wanted a computer to understand an image, you needed metadata ("IMG_001.jpg tags=cat").
Multi-Modal Models (LMMs) like GPT-4o and Gemini 1.5 Pro have solved this. They can "see" pixels and understand them conceptually.
Use Case: Field Service
A technician is fixing a commercial HVAC unit on a roof. They find a rusted valve but can't read the serial number. In the old days, they would drive back to the warehouse or call a senior tech.
The Multi-Modal Workflow:
- Tech snaps a photo with the company app.
- The AI analyzes the geometry of the valve.
- It matches it against the PDF schematics of 10,000 parts in the database.
- It identifies: "This is a Honeywell V5011N."
- It checks inventory: "We have 3 in stock at the downtown depot."
- It links the YouTube video: "Here is the 2-minute guide on how to swap it."
Total time: 15 seconds. First-time fix rate increases by 40%.
Use Case: Insurance Claims
A driver gets into a fender bender. They take a video of the car. The AI analyzes the video frame-by-frame, estimates the depth of the dent, identifies the panels involved, looks up the labor rates for local body shops, and generates a preliminary repair estimate. The claims adjuster just reviews and approves.