Extracting large-scale structured information from complex PDFs is really hard, even with LLMs. 80% accuracy isn’t good enough; a lot of business applications require 98%+. You need specialized capabilities that can not only parse complex tables and charts, but also trace back to the source elements with confidence scores... and citations. We’ve created a best in class document extraction service with LlamaExtract, and you can see for yourself in ~5 mins! Simply log on to LlamaCloud and click on our templates to take a look. Check out LlamaCloud here: If you’re looking to productize document extraction, come talk to us:show more

we built a text based with OCR backup for an engagement with an engineering firm related to building takeoffs from mechanical plans and schedules iterating initially from analyzing multiple thinking traces on an optimized summation prompt we never could have made it work last year the engineering under the hood is startling am unsure where the capabilities edge is now on small and medium size engagements been in the space longer than folks might believe and am still walking into a day with the primary thought of how do I collaborate with this tech for our current projects? How do I polish the craft today? The user is the user manual.

for me, accuracy is just the start

98% grind? crushing it 🚀💯

the 98%+ requirement is where agentic approaches hit a wall. curious if youre seeing validation loops help - having the agent verify extractions against source before committing. RASC research suggests this can be cost-effective but wondering about real-world results

does it work with multilingual handwritten form documents?

Deterministic extraction is the only path to industrial autonomy. 80 percent accuracy is just an expensive way to generate hallucinations for the midwit layer.

crazy

The jump from 80% to 98% in PDF extraction isn't just about better LLM reasoning; it's about structural awareness. Moving beyond visual layout to understanding the semantic intent of nested tables/charts is where real-world utility begins.

Thanks for sharing - this is the real bottleneck. Most people underestimate how messy PDFs are until they try to operationalize them. It’s not just extraction, it’s provenance, structure validation, and handling edge cases at scale. Look forward to checking it out.

80% accuracy kills every enterprise deal

Good point on 80→98% gap, reframes need for provenance, love the focus on confidence scores, hard truth noted, keep refining

The 80→98% gap is where all the real engineering lives. If users can't verify WHERE a number came from in a 200-page PDF, even 95% accuracy feels like 0%

98% is the real bar. that last 2% is where all the engineering hours go to die