Case Study

75% Faster Critical Ticket Resolution Through Intelligent Automation

How a B2B SaaS company transformed their support operations and achieved payback in under 5 months

B2B SaaS50-200 employees United States

Measurable impact from week one

75-80%
Resolution Speed
faster for critical/blocker tickets
<5 months
ROI Payback
investment fully recovered
60%
Manual Search Time
reduction in documentation hunting
50%
Repeat Questions
fewer due to self-service access

The Challenge

Support teams were drowning in repetitive questions, manual ticket triage, and hours wasted searching for answers that already existed somewhere in the organization.

Pain Points

  • Critical tickets sat untouched while agents manually searched documentation
  • Same questions answered repeatedly across Slack, email, and tickets
  • Engineers interrupted constantly with "what changed last sprint?" questions
  • No visibility into which documentation was actually useful vs. outdated
  • Handoffs between shifts caused context loss and duplicate work

Before Automation

Avg. critical ticket response4+ hours
Time spent searching docs35% of shift
Repeat questions per week40+
Context lost in handoffsFrequent

The Solution

We implemented a workflow automation system connecting tickets, documentation, and change history into a unified knowledge layer with intelligent triage and response assistance.

Workflows Automated

  • Automatic ticket categorization and priority scoring based on content analysis
  • Instant knowledge retrieval from internal docs, past tickets, and release notes
  • Smart routing to the right specialist with full context attached
  • Proactive "what changed" summaries for shift handoffs
  • Auto-generated draft responses for common question patterns

Tools Integrated

Jira Service ManagementConfluenceSlackGitHub Release NotesInternal Wiki

Timeline

8 weeks from kickoff to production deployment

How We Did It

1

Discovery (Week 1-2)

Shadowed support team for one week. Mapped every workflow, identified the 3 highest-impact automation opportunities, and prioritized based on effort vs. value.

2

Design (Week 3-4)

Designed the knowledge retrieval system and triage logic. Built integration architecture connecting Jira, Confluence, and Slack. Created human-in-the-loop checkpoints.

3

Build (Week 5-7)

Developed and tested the automation system. Integrated with existing tools—no rip-and-replace required. Established logging and monitoring for every automated action.

4

Deploy & Tune (Week 8)

Rolled out to production with the support team. Trained agents on working alongside automation. Fine-tuned based on real usage patterns.

Beyond the Numbers

Agents report higher job satisfaction—less busywork, more problem-solving
Engineering team sees 70% fewer interruptions for historical context
New hires reach productivity faster with instant knowledge access
Support capacity effectively increased without adding headcount
The automation handles the repetitive stuff so we can focus on actually solving customer problems. It's like having a really good assistant that never forgets anything.
Support Team LeadB2B SaaS Company

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