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Operations & Finance

Which AI agents clean up messy data?

Agents that deduplicate records, normalise fields, enrich rows and pull structured data out of documents and web pages.

53 published use cases

AI agent use cases that clean, enrich and extract structured data

Marketing · Advanced
  1. Keyword in
  2. Scrape top 10 results
  3. Extract page outlines

SEO content briefs from the live SERP

Give it a keyword and get a brief built from the pages that rank today: headings, gaps, questions to answer and a target length.

LangChain · ChatGPT · Apify · Notion
E-commerce · Advanced
  1. Nightly scrape
  2. Match to your SKUs
  3. Apply price rules

Watch competitor prices and reprice nightly

Scrape competitor product pages every night with Apify, match them to your SKUs, and update Shopify prices within margin and change limits you set.

Apify · n8n · ChatGPT · Shopify
Research · Intermediate
  1. Scrape reviews
  2. Tag with ChatGPT
  3. Load into Airtable

Mine 1,000 product reviews for gaps and complaints

Apify scrapes reviews of your products and your competitors', ChatGPT tags each one, and Airtable shows which complaints keep coming up.

Apify · ChatGPT · Airtable

Questions about AI agents that clean, enrich and extract structured data

How do AI agents clean data?

They apply rules you would apply by hand, such as merging duplicates, fixing formats and filling missing fields from a trusted source, but across thousands of rows. Keep the original data and log every change so mistakes are easy to undo.

Which tools do AI agents use to clean, enrich and extract structured data?

Across the 53 published workflows on this page, the most used tools are Claude Code (11), n8n (11) and ChatGPT (4). Each use case lists every tool it needs and the role it plays.

How do I set up one of these workflows?

Open any use case to see the tools, the step-by-step setup and example prompts you can copy. Start with a beginner workflow, run it on a small batch and review the output before you automate it fully.