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AI agent for journalists

Document Dump Triage Agent

Give the reporter a ranked reading list from a large document set with notes and entities.

Document handlingResearchWriters
See how it worksGet access
Document Dump Triage Agent: what goes in, what the agent does and what you get

What it does

A leak or records release can contain thousands of files, and the reporter cannot read them all. This agent sorts the set by type, date and source, extracts names, dates, organizations and amounts, and flags documents likely to answer the story question the journalist writes. It reads the flagged ones and checks that the match is real, not just a keyword hit, and notes what each contains. It then refines its criteria from what the reporter confirms and rejects, and reruns on the full set to find missed documents. It produces a reading list ordered by value, with a short note on each. It never decides what is newsworthy and does not contact anyone. The journalist approves the reading list. Edge case: a scanned document with poor text is flagged for manual review.

How it works

Follow the arrows from top to bottom. The orange dashed arrow is the loop: when a check fails, the agent goes back and tries again.

Start and resultWhat it doesA check on its own workWaits for your OKGoes back and retries
Yes, continueApprovedYes, continueNoNo 1 STARTS WHEN Document set added 2 USES A TOOL Sort files by type, date and source and extract text 3 CHECKS THE RESULT Is the text readable in each file? If not: mark the file for manual review and retryextraction with another method. Back to step 2. 4 DOES Extract names, dates, organizations and amounts 5 DOES Score documents against the story question 6 USES A TOOL Read the top documents and confirm each match 7 DOES Build the reading list with notes 8 YOU APPROVE Journalist marks useful and not useful documents 9 CHECKS THE RESULT Do the confirmed documents show patterns thecriteria missed? If not: refine the criteria and rerun on the full set.Back to step 5. 10 RESULT Ranked reading list and entity index
Read the steps as a list
  1. Document set added
  2. Sort files by type, date and source and extract text
  3. Is the text readable in each file?If not: mark the file for manual review and retry extraction with another method. Back to step 2.
  4. Extract names, dates, organizations and amounts
  5. Score documents against the story question
  6. Read the top documents and confirm each match
  7. Build the reading list with notes
  8. Journalist marks useful and not useful documentsThe agent waits here for your OK.
  9. Do the confirmed documents show patterns the criteria missed?If not: refine the criteria and rerun on the full set. Back to step 5.
  10. Ranked reading list and entity index

How it decides

It scores a document by how many entities and facts in it relate to the story question, and treats a keyword-only match as weak until its content is checked.

  • Rank higher when a document matches two or more key entities
  • Treat a keyword-only match as weak until checked
  • Mark poor scans for manual review
  • Rerun after every batch of feedback

Make it yours

Every agent is a starting point. You choose these settings for your own situation.

  • Story question
  • Entities of interest
  • Date range
  • Reading list size (default 50)
  • Extraction methods

What keeps you in control

It always asks you first

  • The reading list
  • Any contact with sources

Hard limits

  • Never publish or share the documents
  • Keep the original files unchanged

It stops when

  • Done: reporter has what is needed
  • Stop: set is unreadable

Set it up

We guide you through the set-up, step by step

Members get the full set-up guide for this agent. No technical skills needed: you copy, paste and upload.

10 minto set it up in your AI
5 AIsChatGPT, Claude, Copilot, Gemini, Grok
  • One set of instructions to paste into your AI, with the clicks for ChatGPT, Claude, Microsoft 365 Copilot, Gemini and Grok
  • The agent then walks you through connecting your own data, one source at a time
  • A downloadable copy with the flow chart, the rules and the full guide
Get access to this agent

An example run

What happensA county release held 6,400 files. The agent read 5,900, flagged 310 scans as unreadable, and found 212 mentioning the contractor Halvern Group. After the reporter marked 30 of the first 50 useful, the agent noticed they all named a subcontractor, Ridge Paving. A rerun found 87 more documents, including 9 invoices over $50,000. The reporter approved the new list.

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