[epolanski]

At my client we want to provide an AI that can retrieve relevant information from documentation (home building business, documents detail how to install a solar panel or a shower, etc) and we've set up an entire system with benchmarks, agents, etc, yet the bottleneck is OCR!

We have millions and millions of pages of documents and an off by 1 % error means it compounds with the AI's own error, which compounds with documentation itself being incorrect at times, which leads it all to be not production ready (and indeed the project has never been released), not even close.

We simply cannot afford to give our customers incorrect informatiin

We have set up a backoffice app that when users have questions, it sends it to our workers along the response given by our AI application and the person can review it, and ideally correct the ocr output.

Honestly after an year of working it feels like AI right now can only be useful when supervised all the time (such as when coding). Otherwise I just find LLMs still too unreliable besides basic bogus tasks.

[PeterStuer]

As someone who has had a home built, and nearly all my friends and acquaintances report the same thing, having a 1% error on information in this business would mean not a 10x but a 50x improvement over the current practice in the field.

If nobody is supervising building documents all the time during the process, every house would be a pile of rubbish. And even when you do stuff stills creeps in and has to be redone, often more than once.