Home / Case Studies / RAG Chatbot Cuts Response Time 50%: Suncoast Case Study
Suncoast's technicians were constantly pulled off engineering work to field routine product and warranty questions. RTS Labs built a RAG-powered AI chatbot that answers instantly from Suncoast's own manuals, warranty docs, and product data — cutting average response time by 50% and resolving hundreds of inquiries every week.
Conversational AI & Customer Support Automation
Retrieval-Augmented Generation
OpenAI
Vector Search
Web Search Fallback
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Suncoast’s highly skilled technicians were frequently interrupted by routine customer questions about product specs and warranty policies. Every inquiry pulled them away from high-value engineering and repair work, creating longer wait times for customers and lost productivity across teams.
The answers customers needed already existed — but they were scattered across technical manuals, warranty documentation, and product databases, making them slow to surface. Many inquiries ended in callbacks or long hold times, frustrating customers and compounding the drain on technical staff.
Highly skilled techs were repeatedly pulled off engineering and repair work to answer the same routine customer questions about specs and warranties.
Answers lived across technical manuals, warranty documentation, product databases, and web pages — making them slow and difficult to surface on demand.
Many inquiries required callbacks or long hold times, frustrating customers and leaving no way to self-serve a simple answer like a torque spec.
RTS Labs designed a conversational AI chatbot powered by retrieval-augmented generation (RAG). The bot retrieves verified answers directly from Suncoast’s technical manuals, warranty documentation, and product databases — and when a question falls outside those sources, it can safely perform a secondary web search to return an accurate, source-linked response. Every answer prioritizes Suncoast’s own trusted knowledge base first, and a built-in thumbs-up/down feedback loop drives continuous tuning.
Suncoast's technical manuals, warranty policies, and product documentation were ingested, chunked, and indexed into a vector store — creating a verified knowledge base the chatbot could retrieve from in real time.
A retrieval-augmented generation pipeline grounded every answer in Suncoast's own documents first. When internal sources lacked an answer, the bot performed a safe secondary web search — returning responses complete with source links so users always knew where an answer came from.
The bot was scoped to handle both technical questions (fluids, torque specs, product compatibility) and business questions (orders, warranties, returns), always citing source documents so users could verify accuracy and trust the response.
Thumbs-up/down ratings and free-text feedback on every interaction created a natural tuning loop, letting the model improve over time and laying the foundation to scale from proof-of-concept into a full customer-service AI assistant.
Technicians pulled off engineering work to field routine questions
Answers scattered across manuals, documents, and web pages
Customers waited on hold or for callbacks for simple specs
No self-service way to get instant, verified answers
Customers get expert answers instantly, any time of day
Technicians stay focused on complex repairs and development
Hundreds of routine inquiries resolved automatically each week
50% reduction in average response time, with source-linked answers
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