中文
FreeResearch4 nodes
Score 68

Customer Feedback Analyzer

Export reviews or survey data as CSV — LLM classifies sentiment, extracts feature requests and pain points, prioritises by frequency, generates an actionable insight report for product teams.

What does this workflow do?

Drop your csv — the pipeline runs 4 automated steps: Pandas (csv→json) → LLM Reasoning (json→json) → LLM Reasoning (json→markdown) → Python-DOCX (markdown→docx) — and delivers docx. Every node is visible, auditable, and replaceable.

System flow

1. Pandas — Tabular compute — join, aggregate, validate and reshape the extracted records 2. LLM Reasoning — Large-language-model step that extracts, classifies, validates or writes structured output 3. LLM Reasoning — Large-language-model step that extracts, classifies, validates or writes structured output 4. Python-DOCX — Renders the final Word document with headings, tables and styling

Pipeline breakdown

Pandas csv LLM Reasoning json LLM Reasoning json Python-DOCX markdown
  1. Pandas pandas — Tabular compute — join, aggregate, validate and reshape the extracted records csv → json
  2. LLM Reasoning llm — Large-language-model step that extracts, classifies, validates or writes structured output json → json
  3. LLM Reasoning llm — Large-language-model step that extracts, classifies, validates or writes structured output json → markdown
  4. Python-DOCX python-docx — Renders the final Word document with headings, tables and styling markdown → docx

Workflow Reputation Score

Based on executions, success rate, update frequency, component quality, and user rating.

68Overall
★★★★ 4.1
Executions
25
Success rate
95
Update frequency
65
Component quality
85
User rating
82

Based on executions, success rate, update frequency, component quality, and user rating.

Components used

Pandaspandas

Tabular compute — join, aggregate, validate and reshape the extracted records

pandas-dev/pandas ↗
LLM Reasoningllm

Large-language-model step that extracts, classifies, validates or writes structured output

FlowSync proprietary
Python-DOCXpython-docx

Renders the final Word document with headings, tables and styling

python-openxml/python-docx ↗

How is this different from n8n or Zapier?

Unlike generic workflow builders, FlowSync workflows ship pre-configured with AI skills baked into every node — OCR, LLM reasoning, transcription, super-resolution — not just webhook triggers. Each node is white-box auditable: you see input, output, and config. Deployment is instant — no self-hosting, no API-key per node.

Use cases

  • App store review analysis
  • NPS survey processing
  • Support ticket theme extraction
  • Product feedback triage

Author & Source

This workflow is maintained by the FlowSync team. All components are open-source or commercially licensed. Source code for each component is linked in the Components section above.

Pricing: Free tier available · FREE tier · Official

Data handling

Data is processed in-memory during pipeline execution only. No files are stored permanently unless you choose to save outputs to your account.

FlowSync Workflow Marketplace · 68 reputation score · 25% execution · 95% success · 4-node white-box pipeline · maintained by FlowSync Official

Ready to run?

Export reviews or survey data as CSV — LLM classifies sentiment, extracts feature requests and pain points, prioritises by frequency, generates an actionable insight report for product teams.