Automating Customer Support with Retrieval-Augmented Generation

NexaCode Product Team | February 28, 2025

Problem

A SaaS provider with a large support team was handling more than 750 tickets per day. Agents jumped between an aging ticketing system, product release notes, and a fractured knowledge base. The result was inconsistent answers, duplicated effort, and a slow path to resolution for common billing and configuration questions.

When a customer asked about a new feature, agents often responded with outdated policy text. That created extra follow-up messages and eroded trust, especially for high-value accounts.

Approach

We designed a pilot that treated support as a retrieval problem, not a chatbot problem. Our team assembled a corpus from support transcripts, product specs, internal playbooks, and recently updated onboarding docs.

• Extracted and chunked content for intent-specific search.
• Measured recall using actual support queries from the past 90 days.
• Built safe RAG templates with explicit fallback instructions and citation guidance.

Before and after RAG pilot accuracy for support query retrieval.

Impact

Within eight weeks, the pilot reduced the average customer response time and gave agents a reliable answer source. Teams took less time to validate facts and more time to resolve complex issues.

• 40% reduction in support costs through AI-assisted triage and faster case routing.
• 20% increase in first-contact resolution by surfacing accurate, context-aware guidance.
• Human-in-the-loop review for flagged responses, enabling rapid iteration without exposure risk.
• Improved confidence for agents on new products and compliance requirements.

This pilot also created a blueprint for feeding fresh product release notes into the RAG pipeline every sprint, ensuring the system stayed aligned with the latest support content.