AI Research Automation
Turns a recurring research task — gathering and summarizing information from a defined set of sources — into a scheduled workflow with a structured output.
- n8n
- Claude
- APIs
- Google Sheets
The Problem
A recurring research task (competitor tracking, market monitoring, or similar) requires someone to check several sources, read through the material, and pull out the relevant points into a usable summary — every week, by hand.
Existing Workflow
A team member manually visits each source, reads the content, and writes up notes in a shared document, repeating the process on a recurring basis.
The Solution
A scheduled workflow retrieves content from the defined set of sources, extracts the relevant information, and uses AI to summarize and structure it into a consistent format. The structured output is delivered on the same schedule the manual process used to follow.
Automation Architecture
Scheduled trigger -> source retrieval -> content extraction -> AI summarization against a defined structure -> structured output delivery (document or spreadsheet).
Before vs After
Before
A person manually checks each source and writes summary notes on a recurring schedule.
After
Sources are checked and summarized automatically into a consistent structured format on the same schedule.
Results
- Removes the manual source-checking step from a recurring research task.
- Produces a consistently structured summary instead of notes that vary by who wrote them.
Limitations
Works best with a defined, stable set of sources. Sources that require login access or heavy anti-automation measures need a different approach.
Lessons Learned
Defining the exact output structure before building the workflow made the AI summarization step far more reliable and easier to review.
Have a process like this one?
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