When I first started working on an AI-powered project, I assumed the model would be the hardest part. It wasn't. The real challenge turned out to be collecting reliable data from the web.
Like many developers, I started with the usual tools for scraping websites. They worked well for simple cases, but as the project grew, I ran into a few common problems:
Websites loading content dynamically
Different HTML structures across sites
Maintaining extraction logic whenever layouts changed
Converting raw HTML into structured data that my application could actually use.
At some point, I realized I was spending more time maintaining scraping scripts than building the product itself.
Changing My Approach
Instead of thinking:
"How do I scrape this website?"
I started asking:
"How do I build a reliable data pipeline?"
That small change completely changed how I approached the project.
Instead of treating scraping as an isolated task, I started thinking about the complete workflow:
Discover content
Crawl pages
Extract structured information
Process the data
Feed it into the application
Designing the workflow this way made the project much easier to maintain. While experimenting with different approaches, I explored Ollagraph, which combines website crawling, browser automation, search, and structured data extraction through a single API.
What I found useful wasn't simply the ability to scrape websites—it was reducing the amount of custom infrastructure I had to build around the scraping process.
That meant I could spend more time working on product features instead of maintaining extraction logic.
A Few Lessons I Learned
After working through these challenges, a few things stood out:
Structured data is often more valuable than raw HTML.
Planning the data pipeline early saves time later.
Simpler architectures are usually easier to maintain.
The quality of your data directly affects the quality of your AI application.
Final Thoughts
Every project teaches something new.
For me, this one reinforced that building AI applications isn't just about choosing the right model. Reliable data collection and a maintainable workflow are just as important.
I'm still experimenting with different tools and approaches, and I'd be interested to hear how others are solving similar challenges.
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