AI Support Agent Cuts Ticket Volume 42% for a B2B SaaS Platform

We built an AI support agent grounded in the company's own help center, API docs, and past resolved tickets using a retrieval-augmented approach, so every answer was based on current, accurate product information rather than the model's general knowledge. Rather than sending every request to a single expensive model, we designed a routing layer: a small, fast model handles classification and routine replies, while a flagship model is reserved for complex, multi-step issues. This kept response quality high while holding per-ticket cost down. Key elements of the build: - Retrieval over help docs, API references, and historical ticket resolutions - A model-routing layer that escalates only hard tickets to a flagship model - Guardrails and a confidence threshold that hands off to a human whenever the agent is unsure - Deep integration with the existing help desk so the agent works inside current workflows We launched with a human-in-the-loop phase, measured the agent against an evaluation set of real tickets, then progressively widened its authority as accuracy was proven. Support engineers shifted from answering repetitive questions to supervising the agent and handling genuinely complex cases.