AI Assistant Reduced Support Response Load

Metrics are published with implementation context, stack details, timelines, and result summaries.

A SaaS support team needed to reduce repetitive tier-1 tickets without allowing an AI assistant to answer outside approved help content.

Knowledge-base ingestion pipeline with document review before indexing.

RAG assistant connected to Zendesk and Slack with confidence-based routing.

Human handoff workflow that attaches AI context and a drafted response.

Weekly answer-quality audit loop for prompt, retrieval, and content updates.

Next.js

OpenAI

Vector database

Zendesk

Slack

PostgreSQL

Vercel

8-week rollout: 2 weeks discovery, 3 weeks build, 1 week integration, 2 weeks pilot and tuning.

Analyzed 12 months of support ticket history to identify the top 15 most repetitive and automatable support intents.

Developed a grounded 'RAG-based' AI Assistant that only utilizes the client's verified internal knowledge base to generate responses.

Integrated the assistant directly into Zendesk and Slack, with a 'Seamless Handoff' protocol to human agents for complex queries.

Implemented a 'Confidence Score' gate—if the AI is less than 85% certain, it immediately routes the ticket to a human with a drafted suggestion.

Established a weekly 'AI Quality Audit' where support leads review and 'thumbs-up/down' assistant responses to fine-tune the underlying prompts.

38% ticket deflection for repetitive tier-1 requests.

Median first response improved from hours to minutes.

Human agents retained control of low-confidence or sensitive tickets.

Support cost reduction came from deflecting repetitive tickets and letting agents focus on complex cases.