Dec '25 • General discussion
I built a Data Quality AI Agent using n8n + AI
Data quality problems usually show up after decisions are made—missing values, invalid records, and numbers nobody trusts.
So I built a Data Quality AI Agent that changes how teams interact with data.
✅ Automatically cleans and validates raw data
✅ Separates clean and failed records (nothing is hidden)
✅ Tracks data quality metrics in real time
✅ Lets users ask questions in plain English via Telegram
✅ Clearly explains whether an answer comes from clean or failed data
Example:
💬 “What is the salary of Neha?”
🤖 “Neha’s salary is 80,000. This record was found in clean data.”
💬 “What is the salary of Jonathan?”
🤖 “Salary is 0. This record was found in failed records due to invalid salary.”
No dashboards. No SQL. No guessing.
Built using:
- 🧩 n8n for workflow orchestration
- 🤖 AI Agent (LLM) for natural language understanding
- 📊 Google Sheets as a transparent data layer
- 💬 Telegram as the chat interface
The goal wasn’t just automation — it was trust.
If you’re exploring:
✔ AI Agents
✔ Data Quality Automation
✔ n8n workflows
✔ Conversational analytics
Would love to hear your thoughts 👇
#DataQuality #AIAgents #n8n #Analytics #Automation #DataEngineering #AIinBusiness
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I built a Data Quality AI Agent using n8n + AI
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