IMO, a real solution here has to be hybrid, not full LLM, because these sheets can be massive and have very complicated structures. You want to be able to use the LLM to identify / map column headers, while using non-LLM tool calling to run Excel operations like SUMIFs or VLOOKUPs. One of the most important traits in these systems is consistency with slight variation in file layout, as so much Excel work involves consolidating / reconciling between reports made on a quarterly basis or produced by a variety of sources, with different reporting structures.
Disclosure: My company builds ingestion pipelines for large multi-tab Excel files, PDFs, and CSVs.
That's exactly what they're doing.
https://www.anthropic.com/news/advancing-claude-for-financia...
"This won't work because (something obvious that engineers at Anthropic clearly thought of already)"
Not really. Take for example:
item, date, price
abc, 01/01/2023, $30
cde, 02/01/2023, $40
... 100k rows ...
subtotal. $1000
def, 03/01,2023, $20
"Hey Claude, what's the total from this file? > grep for headers > "Ah, I see column 3 is the price value" > SUM(C2:C) -> $2020 > "Great! I found your total!"
If you can find me an example of tech that can solve this at scale on large, diverse Excel formats, then I'll concede, but I haven't found something actually trustworthy for important data sets
That's a basic tool call that current models already can do well. All the sql query generation LLMs can do this for example.
So more or less like what AI has been doing for the last couple of years when it comes to writing code?