An automated system for classifying customer inquiries, monitoring SLA compliance with escalation, and performing AI-driven analytics — both quantitative and qualitative — built with n8n and an LLM.
Project Details
Context:
- Business/industry: IT & SaaS / Customer Support Services
- Current process: Customer requests arrive in a shared Gmail inbox. A manager manually assesses urgency, decides who should receive each ticket, and tracks response deadlines by hand. Management had no tool to analyze request volumes or identify recurring issues — only scattered emails in Gmail.
Problem:
- Classifying and prioritizing requests took too much time and depended on the manager’s subjective judgment.
- There were no formal SLA standards or a mechanism to monitor compliance.
- Overdue requests were discovered by chance, often only after a customer complaint.
- There was no analytics on request volume — how many requests were received, in which categories, and with what sentiment.
- Management had no tool to understand the root causes of recurring issues, and producing any summary report required manually reviewing all tickets.
Solution
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The system automatically receives customer requests, classifies them using AI with an explanation of the decision, logs them, and notifies the ticket owner. A separate SLA timer monitors deadlines with a three-level escalation process. On top of this, an analytics layer was built: quantitative metrics and qualitative AI-based pattern analysis, accessible through a conversational AI agent (chatbot) on demand.
Tools used: OpenAI API, n8n, Zendesk API, Slack Webhooks, PostgreSQL.
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Solution logic:
- A Gmail trigger captures new customer requests.
- An AI classifier (chain of thought) determines the category, priority, owner, and SLA.