Claude Code
Best for: Serious work on real repos
Anthropic's agent for your terminal, IDE and desktop. Reads the whole repo, runs commands and tests, and handles large multi-file changes well.
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Find patterns in customer feedback
“We have reviews, tickets and interviews but no time to read them all.”
AI can sort feedback into themes, count how often each comes up, and pull real quotes. Remove personal info first, and spot-check that quotes are real.
Fix it yourself in 3 steps
- Gather feedback (tickets, reviews, survey answers, interview transcripts) with names removed.
- Ask the AI to group it into themes with counts and example quotes.
- Check a few quotes against the source, then pick the top 3 problems to fix.
Copy this prompt
Here's customer feedback with personal info removed: [files]. Group it into themes. For each: count, 2 exact quotes with source ID, and how severe it seems. Rank the top 5 by how often and how painful. Don't invent quotes — every quote must appear in the source. Stop after the list.
Fill in the [brackets]. Each prompt says when the AI should stop, so it doesn't wander off.
Agents people used for this: Claude Code, Perplexity Comet, Zapier Agents, Grok Bot.
Inspired by real examples collected at agentusecases.ai.
📚 6 real stories of people doing this with AI agents
- Find Reddit threads on reader pain points · Perplexity Comet
- Flag churn and expansion signals and draft support replies · Zapier Agents
- Mine Reddit threads into structured Notion reports · n8n | Gemini
- Research Reddit customer pain points with a skill · Codex
- Review support tickets and synthesize product feedback · Grok Bot
- Synthesize interview transcripts and PDFs for research · Claude Code
Collected at AgentUseCases.ai.
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