August 11, 2025
Recently I worked on a fascinating project that involved processing image data at scale to extract structured information. The use case? Converting advertisements into a comprehensive list of leads. What started as a straightforward image-to-text conversion turned into a masterclass in practical AI implementation.
The Process
The workflow broke down into four main phases:
- Scanning documents - Gathering all the advertisement images that needed processing
- Prompt engineering - Creating a prompt that would reliably convert image data into structured data
- Processing at scale - Running the conversion across all documents efficiently
- Data import - Getting the structured data into the destination system for use
Key Lessons Learned
Multiple Data Sets Per Image
One of the most surprising discoveries was that you don't need to process images one-at-a-time with discrete data sets. A single image might contain 4 different advertisements or data sets, and the AI had no problem identifying and extracting each one separately. This was a huge efficiency gain - instead of pre-processing images to isolate individual items, we could feed in composite images and get back properly structured, separated data.
Flexible Data Extraction
Getting the AI to pull out different types of data - names, websites, phone numbers, addresses - turned out to be surprisingly easy. With the right prompt structure, the model could reliably identify and categorize different data types without extensive fine-tuning or multiple passes. The key was being clear about the expected output format and providing good examples in the prompt.
Cost-Effectiveness
Despite the common perception that AI image processing is expensive, the actual costs were quite reasonable. Yes, AI uses computational power and processes significant amounts of data, but for a project like this where you're extracting real business value (qualified leads), the cost per conversion was well within acceptable bounds. The time saved compared to manual data entry made it a clear win.
Results
This was actually a pretty fun project. The combination of practical business value (generating leads), technical problem-solving (scaling the processing), and AI capabilities (multi-item extraction from complex images) made it engaging from start to finish. More importantly, it demonstrated that AI image processing is ready for production use cases today - not in some distant future.
If you have image data that needs to be converted into structured, actionable information, it's worth exploring what modern AI can do for you. The technology has matured to the point where implementation is straightforward and the results are reliable.
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