Fixing Overwhelmed Support Teams with Agent-First AI Chatbots

The Problem

A large customer support operation was struggling under consistently high ticket volumes, long handling times, and inconsistent responses across multiple knowledge sources. Customers waited too long for answers to basic enquiries, agents were tied up with repetitive tasks, and performance metrics such as SLAs and CSAT were declining. The organisation needed a scalable way to automatically resolve simple queries while preserving full conversational context for complex cases that required human intervention.

The Solution

One of our digital squads deployed an agent-first support layer powered by retrieval-augmented generation, agent orchestration, and full CRM integration. The rollout began with stakeholder discovery and a focused proof of concept before being extended into live support channels. The AI agent used a RAG pipeline indexing ticket history, product documentation, and knowledge-base content into a vector store, enabling accurate, cited responses. When the AI detected a case beyond its confidence threshold, it passed the enquiry to a human agent along with structured context, suggested actions, and conversation history. Operational safeguards were integrated throughout, including human-in-the-loop validation, automated retraining cycles using confirmed responses, and analytics that surfaced recurring issue patterns.

The Impact

55% of incoming queries resolved by the AI agent on first contact
30–50% reduction in average handle time for agent-assisted tickets
CSAT uplift of approximately 12 points for routine enquiries
Reallocation of the equivalent of ~0.8 FTE per 1,000 weekly tickets to higher-value work
Faster onboarding for new agents using AI-generated suggestions and context cards