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B2B SaaS · Support2025AI AutomationCustom AI Integration

Support ops drowning in tickets → triaged in seconds

First-response time
−92%
Tickets auto-triaged
11k/mo
Time reclaimed
3 FTE

The problem

A support team of nine was manually reading, tagging, and routing every inbound ticket. Backlogs grew nightly and SLAs slipped, especially across timezones.

The approach

  1. Mapped the full triage workflow with the team and instrumented the queue.

  2. Built a classification + routing pipeline that tags urgency, product area, and sentiment.

  3. Grounded suggested replies in their help-center via RAG, with a human approve step.

  4. Added dashboards + alerts so leads see throughput and drift at a glance.

Stack

  • Python
  • LangChain
  • Vector DB
  • PostgreSQL
  • Redis
  • Docker
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