Client Context
A logistics software provider serving mid-market and enterprise customers across North America. The platform manages complex, configuration-specific workflows for each client, meaning support queries often require deep product knowledge to resolve accurately. The support team handled thousands of tickets per month across email, chat, and a ticketing portal.
The Problem
The challenge was not headcount. The support team was experienced and well-staffed. The problem was the research overhead attached to every ticket. Because the product was highly configurable, agents needed to locate the right documentation for the specific customer configuration before they could even begin writing a response. Less experienced agents spent 15 to 30 minutes per ticket doing research that senior agents completed in minutes — not because the answer was difficult, but because finding it was. This inconsistency created unpredictable resolution times and periodic escalation backlogs.
What We Built
We built an AI support engine that connects directly to the organization's product documentation, historical ticket archive, and customer configuration data. When a ticket arrives, the system retrieves the relevant context for that specific customer, product version, and issue type, and either drafts a complete resolution or routes the ticket to the appropriate specialist with the context already assembled. The system distinguishes between known-issue patterns and genuinely novel problems, applying different handling to each.
Technical Approach
- RAG-based knowledge retrieval across product documentation and historical ticket archive
- Customer-specific context injection using configuration data at query time
- Multi-model orchestration routing tasks by complexity and confidence
- Intelligent ticket classification and specialist routing with pre-written context summaries
- Confidence scoring with automatic escalation for low-confidence or novel inputs
- Full audit logging on every AI-generated response and routing decision
Timeline
Pilot on a single product category: 3 weeks. Full production rollout: 12 weeks.
The Outcome
Ticket volume dropped 35% through intelligent deflection of known-issue queries that the system now resolves without agent involvement. For tickets that do reach agents, resolution time is 50% faster because agents receive a context summary rather than starting from a blank screen. The support operation now scales without proportional headcount growth — a meaningful structural improvement for a company expanding its customer base.
What set TTG apart was that they understood our product and our customers before they wrote a single line of code. The AI support system they built did not just reduce ticket volume — it gave our agents better context than they had before automation. That is a hard thing to achieve.
VP of Customer Operations, Logistics Software Provider (North America)