Why do general-purpose extraction tools keep falling short for event data?

I’ve been looking closely at tools like PulpMiner, Firecrawl, and even throwing Gemini directly at the problem — and honestly, they’re all interesting in their own way.

But when it comes to event data specifically, I keep seeing the same issues pop up:

• 🔁 A lot of repeated effort across domains that almost behave the same

• 🤖 LLM-driven approaches that are expensive, inconsistent, or need babysitting

• 🧩 Tools that solve extraction but not discovery (or vice versa)

• 🧹 Time spent cleaning up results instead of using them

They all help… but none fully eliminate the wasted effort.

So I’m curious from a technical perspective:

• Where do you think most of the inefficiency actually comes from?

• Is it discovery, interpretation, or validation?

• When do LLM-only approaches make sense, and when do they just burn tokens 💸?

• What would a “boringly reliable” event pipeline even look like?

Feels like there’s still a big gap between possible and practical here — and closing that gap could save teams a ton of time and money 🚀

Would love to hear how others are thinking about this. 👇🏾