The data is in there. It’s just trapped in the mess.

The numbers you want are scattered across emails, PDFs, filings, and web pages, in slightly different formats every time. Tell the assistant what fields matter and it extracts them into one clean tracker, archives the originals, and keeps it deduplicated as new sources come in.

What it does

• Takes a plain description of what you’re tracking and the fields you want (dates, amounts, names, statuses, whatever the case needs).
• Searches the sources you point it at: connected email, dropped-in documents, public filings and pages.
• Extracts the fields into a structured table, saving each raw source first so nothing is invented after the fact.
• Deduplicates as it goes, skipping exact repeats and flagging near-matches for you to eyeball.
• Keeps a canonical tracker with running totals, every row linked back to the source it came from.

The reveal

You’ve got two years of update emails you never turned into anything. You describe the five fields you wish you had in a spreadsheet. By the end of the day there’s a clean table: every entry extracted, every number linked back to the original email, the duplicates already merged, and the totals at the bottom. The pile you kept avoiding is now something you can actually read.

What it doesn’t do

• It doesn’t guess. If a field isn’t in the source, the cell stays empty rather than getting a plausible invention.
• It won’t trust its own memory for the numbers. Every figure is read back from the saved source, not recalled.
• It isn’t analysis. It builds the clean dataset; the conclusions are yours to draw.

Configuration

• What to track: the subject and the exact fields you want as columns.
• Where to look: which connected accounts and public sources to pull from.
• How to dedupe: what counts as the same entry versus a genuine update.

Triggers

On-demand, and optionally recurring: set it to sweep new sources on a schedule and keep the tracker current.