Ambiguous Case Handling

Step 1 - Data Loading

Loaded 4 requests. Required columns present: request_id, title, description, source, customer.

Step 2 - Duplicate Analysis

Ambiguous Cases (70-85% confidence) - Manual Review Needed:

• Request #1 “Mobile notifications” vs #2 “Real-time alerts” (72%)

  • Reason: One is generic/real-time notifications, other is channel-specific
  • Recommendation: Review manually - likely different features

• Request #2 “Real-time alerts” vs #3 “Slack alerts” (72%)

  • Reason: One is generic/real-time notifications, other is channel-specific
  • Recommendation: Review manually - likely different features

• Request #2 “Real-time alerts” vs #4 “Email notifications” (72%)

  • Reason: One is generic/real-time notifications, other is channel-specific
  • Recommendation: Review manually - likely different features

Step 3 - Deduplicated List

Total unique requests: 4

1. Mobile notifications (1 mention)

   • Source: email
   • Customer: AlphaCo

2. Real-time alerts (1 mention)

   • Source: support
   • Customer: BetaCo

3. Slack alerts (1 mention)

   • Source: email
   • Customer: GammaCo

4. Email notifications (1 mention)

   • Source: support
   • Customer: DeltaCo

Step 4 - Theme Clustering

Theme: Notifications & Alerts (4 requests, 100%)

• Mobile notifications
• Real-time alerts
• Slack alerts
• Email notifications

Step 5 - Key Insights

• 3 ambiguous case(s) flagged for manual review
• All requests fall within Notifications & Alerts theme
• Each request targets a different delivery channel (mobile, Slack, email) or is generic

Recommendation

These requests represent distinct features for different notification channels.
Consider consolidating into a unified notification system with channel-specific implementations:

• Notification infrastructure (core)
• Mobile push notifications
• Slack integration
• Email alerts
• Real-time delivery framework