Show HN: We had AI write a wiki from 45k Enron emails, every fact cited (thisandthat.chat)

It's not so much related to Enron or what a historic corporation did right or what a historic corporation did wrong, although those aspects are interesting too...

What's interesting is the use of an AI, an LLM, to create a "knowledge map" (in this case a Wiki, but it could have taken other forms) of a whole bunch of disjoint, separate, and apparently (at first glance, to the naked eye of a casual observer) disparate information from different emails at different dates and times to different parties outside of the company, the only common thread being that they were sent by employees of the same company, and for the AI/LLM to be able to extract facts and a knowledge graph ("who knew what, when") again, in the form of a Wiki (but could have been in another format), and this allows "making sense of", some "higher level viewpoint of" (at least to some better degree than all of the emails alone), again, in this case, in the form of a Wiki.

Now, this set of ideas could have some application far beyond Enron, far beyond investigations, far beyond what Enron historically did right or Enron historically did wrong...

For example, large companies and other organizations could create Wikis based on public facts in email corpuses for onboarding new employees. Not just for email interactions with outside companies (customers, suppliers, etc.) but for how different systems, different business processes used by the company work, or are supposed to work. Sort of like new employee manuals where there are entries like "if this fails, then do this...", or here's our documentation which shows that we're ISO-9000 compliant, etc. etc.

Point is, the amount of uses for something like this in a corporate setting is theoretically unlimited.

Of course, there might be issues as to what information different types of employees could read about, that is, larger corporations may wish to set access controls to different types of information, but there's no reason why an AI/LLM couldn't perform this function (with human oversight!) as well. Each of those could be ironed-out on an per-company, per-case, ad-hoc basis...

But again, the point is, the amount of potential uses for something like this in a corporate setting is theoretically unlimited...

Anyway, great article, and great idea!

2002, Arthur Andersen gets indicted for deliberately aiding Enron in cooking the books. 2003, Bush DoJ's attorney general binds prosecutors to effectively always settle corporate cases without convictions. So nowadays you have companies like Boeing murdering 300+ without real consequences. It's a real shame.

Enron's mail is an obvious test because it's public, so anyone can check the pages against the messages and the history. We used 45,047 emails from eight mailboxes. Every fact quotes the sentence it came from, and a fact expires on the date the mail shows it stopped being true.

The hardest part was that the model already knows Enron. It kept adding details from the public record that the email didn't contain. The methodology page shows how we caught those, what it still gets wrong and what it cost: about $11,000 at list prices (though we used a subscription), with no attempt to bring that down.

Happy to answer questions.