I'm curious about the 60% automation of financial/forensic analysis - what's missing? Is it stuff that's purely blocked by model capabilities, or are there places where scaffolding is likely to bridge the gaps?

Also curious about the workflow - is this more individual, LLM-driven features or agentic workflows? Looked like the former from the product video but there wasn't a ton of UX shown there.

I ask largely because this seems like the sort of thing where you could really start to string these features together in such a way that you start with a description of the case and whatever files you have, and then an agent does its analysis of the docs, spins up action items (get missing docs, confirm that X ambiguous doc is what the AI characterized it as, etc.) and tracks the progress of all of them, leaving your forensic accountant there in a supervisory role, managing and providing expertise.

It feels like that's the way a lot of expert analysis jobs like this are headed. I've been working on the same sort of flow to use agents to manage my business. Started with LLM skills that could be used to handle tasks I used to do myself, and since then I've increasingly been having AI use those skills on its own without me invoking them and chain things together into full blown workflows. Some parts I'm still supervising closely, but others that have been working consistently for a while I now don't really watch unless Claude flags something for me to review on my dashboard.

submitting private information to LLMs w/no privacy guarantees is probably a crime btw

Here is my hot take. AI is going to replace some developers (not all) and the first ones it replaces will be the ones who can't code without it. The developer in this story provided a relationship with a forensic accountant, a few discussions with paralegals, and limited guidance to an agent. The agent did literally everything else, including writing the article!

1. https://news.ycombinator.com/showhn.html