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Case Study

AI Support Agent for a High-Volume SaaS

A LangChain support agent that deflects repetitive tickets and routes the rest to the right human, fast.

LangChainOpenAIVector searchNext.jsSupabase
AI Support Agent for a High-Volume SaaS

The Challenge

A growing SaaS support team was buried under repetitive questions — password resets, billing queries, and “how do I” requests — that pulled senior staff away from complex cases and slowed first-response times.

Our Approach

  • Ground the assistant in the product docs and help center using retrieval, so answers are accurate and cite their source.
  • Add confidence thresholds so the agent refuses to guess and escalates weak matches to a human.
  • Route unresolved conversations to the right queue with the full context attached, instead of a cold handoff.

What We Built

We built a retrieval-augmented support agent that answers common questions instantly from the knowledge base, collects structured details before escalating, and hands the conversation to a human with context when it is unsure. Every unanswered query is logged so the team can close content gaps over time.

The Outcome

  • Repetitive, well-documented questions are deflected without human involvement.
  • First-response time drops because routine tickets never enter the queue.
  • Senior support staff spend their time on genuinely complex cases.
Representative of the type of work Baydot delivers. Specifics are anonymized; we scope real numbers and constraints for your project on the call.

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