Project Overview
A Fortune 500 retailer came to us with a familiar problem: their customer support team was drowning in tickets, and off-the-shelf chatbot solutions were not cutting it. The existing system could not handle product-specific queries, returned generic answers, and frustrated both agents and customers. They needed something that understood their catalog, policies, and tone, not a wrapper around GPT.
We embedded with their engineering and support ops teams for a two-week discovery sprint. From there, we designed a retrieval-augmented generation (RAG) pipeline backed by a fine-tuned LLM, connected to their product database, order management system, and internal knowledge base. The architecture was built for low-latency inference at scale, with fallback routing to human agents when confidence scores dropped below threshold.
Within eight weeks, the system was live in production handling 60% of inbound queries autonomously. Average resolution time dropped by 40%, and customer satisfaction scores improved by 18 points. The support team shifted from reactive ticket-clearing to proactive relationship management.
Post-launch, we stayed on for a three-month iteration cycle, monitoring model drift, expanding the knowledge base, and adding multi-language support. The system now processes over 50,000 queries per week with a 92% resolution rate.
Key Takeaways
- RAG pipelines outperform generic chatbots for domain-specific queries
- Confidence-based routing keeps humans in the loop where it matters
- Iterative post-launch tuning is critical for sustained performance
- Production AI requires robust monitoring and drift detection
Challenge
The retailer's support team handled over 80,000 tickets per month. Off-the-shelf chatbots returned generic answers that frustrated customers and increased agent workload. They needed an AI system that understood their product catalog, return policies, and brand tone.
Strategy
We conducted a two-week discovery sprint embedded with engineering and support ops. We identified RAG as the optimal architecture pattern and designed a pipeline with confidence-based routing to balance automation with human oversight.
Solution
We built a production-grade RAG pipeline backed by a fine-tuned LLM, integrated with the product database, order management system, and internal knowledge base. The system includes automated drift detection, multi-language support, and a real-time analytics dashboard for the support team.
Retail Engagement
(Confidential)