Intelligent Automation of Customer Requests Received by Email

The organization faced a high volume of customer requests received through email, with most of the processing handled manually. Agents were responsible for interpreting messages, entering information, consulting multiple systems, prioritizing requests, and creating or updating tickets.

This resulted in high volumes of manual work, long first-response times, repetitive system queries, incomplete or inconsistent tickets, difficulty prioritizing requests, and increased operational workload during demand peaks.

The organization needed an intelligent layer capable of transforming unstructured emails into structured customer requests that could be processed and acted upon automatically, while preserving human intervention for complex or sensitive cases.

Rootstack implemented an AI-powered customer service automation engine using Claude, n8n, PostgreSQL, Python, REST APIs, webhooks, Docker, and OpenTelemetry. The core workflow followed the sequence: Email → Intent Understanding → Data Extraction → Customer Identification → System Query → Workflow Execution → Ticket/Response → Human Escalation.

Claude analyzed the complete context of incoming messages to understand customer intent beyond simple keyword matching. The system could identify requests related to balances, products, customer data, documents, transactions, application status, complaints, policies, and service modifications.

Once the intent was identified, the platform extracted and normalized relevant information such as customer IDs, account numbers, policy numbers, ticket numbers, transaction references, dates, products, and request descriptions. This structured information was then validated before being passed to downstream systems.

The solution could identify the customer and retrieve authorized information through APIs, including application status, account details, transaction status, policy information, case history, and available documentation. The LLM did not access transactional databases directly; instead, controlled APIs and tools maintained a clear separation between the AI layer and enterprise systems.

When a request could be resolved automatically, the system generated responses based on real information retrieved from authorized enterprise systems. For requests requiring follow-up, it automatically created or updated service desk or CRM tickets containing the identified intent, customer information, AI-generated summary, relevant history, priority, category, and applicable SLA.

Intelligent prioritization used business rules based on request type, customer profile, affected product, urgency, SLA, potential impact, and existing case history. Cases outside the configured scope, requiring human judgment, containing inconsistent information, or involving sensitive processes were escalated to the appropriate team with the case already understood and enriched.

n8n orchestrated the complete workflow, from email reception and preprocessing through intent identification, information extraction, API-based retrieval, validation, action execution, ticket or response generation, escalation, and result logging. Its modular architecture allowed new workflows to be introduced without modifying the core language-understanding engine.

The project was completed in 6 months and achieved 55–65% automation of customer requests, with an estimated 65% reduction in first-response time and 60% reduction in manual tasks. Incomplete tickets were reduced by an estimated 70%, while ticket creation time was reduced by approximately 80%. The solution operates 24/7, supports human escalation for complex cases, and was deployed progressively by request type.