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SLA INTELLIGENCE CENTER

"We need to see the real picture — not just the numbers"

Here's the thing about SLA dashboards — they only tell you what happened, not why it happened. Your 95% compliance looks great on paper, but dig a little deeper and you'll find clock manipulation, workload imbalances, process bottlenecks, and assignment gaps hiding behind those numbers.

Our AI doesn't just track SLAs — it understands them. It detects when someone's gaming the system, spots overloaded engineers before they burn out, identifies which categories always breach, and recommends exactly what to fix.

And here's what makes it special: Every detection comes with a clear explanation. You'll know exactly why the AI flagged something — the data points, the patterns, the reasoning. No black boxes, no guesswork.

Fraud Detection
Workload Balance
Bottleneck Analysis
Process Optimization
47
Fraud Cases
Detected
34h
Suspicious
Pause Time
12
Hidden
Breaches
8.7%
SLA Impact
Live: AI Detection Feed
🎫 TKT-4521 | P2 | Yardi System Login Failure
Property: DAMAC Hills - Golf Vista Tower
Requester: Leasing Dept | App: Property Sales Portal
Status: "Waiting for User" at 14:47
SLA Breach Due: 15:00 (13 min remaining)
⚠️ No outbound email to requester found
⚠️ Generic comment: "Waiting for user"
🤖 AI Verdict: 87% Suspicious
📋 Action: Flagged - Escalated to Fatima Hassan (Sr. Applications)
How It Works
Powered by SCIKIQ GenAI

AI Detection Categories 5 Active Monitors

All Systems Healthy
12 Found
SLA Gaming & Fraud

Clock manipulation, fake status changes, hidden breaches

3 High Risk
8 Found
Workload Imbalance

Overloaded engineers, uneven distribution, burnout risk

2 Engineers at risk
5 Found
Process Bottlenecks

Categories with chronic delays, approval backlogs

VPN avg 4.2h delay
15 Found
Assignment Gaps

Unassigned tickets, skill mismatches, routing errors

7 pending >2hrs
3 Predicted
Breach Predictions

ML-predicted breaches in next 4 hours

94% accuracy
Detection Types: What We Monitor

Our AI continuously monitors four key detection types: SLA Fraud (clock manipulation, gamed responses), Workload Balance (overloaded engineers, capacity issues), Process Bottlenecks (category delays, application issues), and Assignment Gaps (misrouted tickets, specialty mismatches). Each flagged ticket gets a detailed risk analysis.

How It Works: Our Detection Algorithms

Every AI detection comes with complete transparency. Below, you'll see exactly how we analyze SLA tickets — the specific rules, weighted calculations, and reasoning behind each detection. Switch between Fraud, Workload, Bottleneck, and Assignment algorithms to understand what triggers our alerts.

How Our AI Detects Issues — Full Explainability Transparent AI

SLA Fraud Detection Algorithm Type 1
1
Communication Verification
35% weight

When status changes to "Waiting for User/Approver", we check:

  • Was an email sent to the user within ±2 hours of status change?
  • Does the email contain ticket-specific content (not a template)?
  • Is there a valid recipient email address?
IF status_change == "Waiting" AND no_email_found THEN score += 35%
2
Near-Breach Timing Analysis
30% weight

Suspicious timing patterns:

  • Status paused within 2 hours of SLA breach deadline
  • Multiple pauses on same ticket approaching different SLA milestones
  • Pattern of pauses at end of shift
IF time_to_breach < 2h AND pause_triggered THEN score += 30%
3
Comment Quality Analysis
20% weight

GPT-4 analyzes comment quality:

  • Generic phrases: "Waiting for response", "Pending user reply"
  • Copy-paste from templates without customization
  • No specific details about what was asked
IF GPT4.isGeneric(comment) > 0.7 THEN score += 20%
4
Engineer History Pattern
15% weight

Historical behavior analysis:

  • Number of previous flagged tickets (30 day window)
  • Pause frequency compared to team average
  • SLA save rate (breaches avoided by pauses)
IF previous_flags > 2 THEN score += 15%
Live Detection: DAMAC Leasing Ticket
TKT-4521 | Yardi Login Issue 87% Suspicious
DAMAC Hills - Golf Vista | Leasing Dept | Assigned: Mohammed Khan
No email sent to leasing agent +35%
Paused 13 min before P2 breach +30%
Generic: "Waiting for user response" +15%
Engineer: 2 prior flags this month +7%
AI Reasoning: High confidence of SLA gaming. Leasing agent at Golf Vista reported Yardi login failure affecting property viewings. Status changed to "Waiting" 13 min before breach with no email contact. Pattern matches 2 prior incidents from same engineer affecting Property Sales Portal operations.
Detection Thresholds
Auto-Flag Threshold
Tickets above this score are flagged
40%
High Risk Threshold
Requires immediate manager review
70%
Email Window
Time window to check for emails
±2 hours
SLA Health Dashboard: Real-Time Analytics

See the reality behind your SLA numbers. Compare reported compliance vs. true SLA performance, identify which categories breach most frequently, analyze workload patterns, and discover process bottlenecks. Real-time metrics update continuously as the AI processes new tickets.

Real-Time SLA Health Command Center LIVE

Updated 30s ago
SLA Compliance: Reported vs True
95%
Reported
(What dashboard shows)
82%
True (AI-Adjusted)
(After fraud removal)
13% gap = Hidden SLA breaches
True SLA by Priority
P1 Critical 78%
Target: 99% | Gap: -21%
P2 High 84%
Target: 95% | Gap: -11%
P3 Medium 89%
Target: 90% | Gap: -1%
P4 Low 92%
Target: 85% | +7% above target
Breach Predictions (Next 4hrs) AI
TKT-4892 - Payment Gateway
P1 - Property Sales Portal | Downtown Dubai
94%
in 47 min
TKT-4901 - Yardi Timeout
P2 - Leasing Team | DAMAC Hills
76%
in 1.5 hrs
TKT-4887 - CRM Sync
P2 - Customer Relations | Business Bay
58%
in 2.3 hrs
SLA Fraud Impact Calculator Business Value
$287K
Hidden SLA Penalty Exposure
Per Year (if undetected)
$341K
Saved by Early Detection
Penalties avoided this year
Fraud Impact Breakdown
23
Hidden P1 Breaches
156h
Suspicious Pause Time
8
Engineers Flagged
Contract Protection
Accurate SLA data for vendor negotiations
+12% leverage
GenAI Decision Explainability Transparent AI
TKT-4521 | Yardi System Login 87% Suspicious
DAMAC Hills - Golf Vista | Leasing Dept | Property Sales Portal
No Outbound Email
Status changed to "Waiting" but no email sent to Leasing agent
+35% to suspicion score
Near-Breach Timing
P2 SLA paused with only 13 minutes remaining - Property Sales impacted
+30% to suspicion score
Generic Comment
"Awaiting customer response" - matches placeholder pattern
+15% to suspicion score
Engineer: Mohammed Khan (Service Desk)
2 previous flags on Property Management tickets this month
+7% pattern bonus
AI Recommendation
Escalate to Service Desk Manager. This Yardi ticket affecting DAMAC Hills leasing operations shows SLA clock manipulation. Recommend: 1) Correct SLA timer (+13 min), 2) Coaching session with Mohammed Khan, 3) Alert Fatima Hassan (Sr. Applications) to verify Yardi system status.
Enterprise Compliance & Audit Trail SOC2 Ready
2,847
Tickets Audited (30 days)
47
Fraud Cases Detected
98.3%
Audit Accuracy Rate
24/7
Continuous Monitoring
DAMAC Properties - Recent Audit Events
FLAG TKT-4521 (Yardi Login - DAMAC Hills) flagged for SLA manipulation
2 min ago
ALERT Mohammed Khan (Service Desk) - 3 flags threshold | Manager notified
15 min ago
CLEAR TKT-4498 (Payment Gateway - Business Bay) reviewed - legitimate pause
1 hr ago
BOTTLENECK Property Sales & Marketing SLA breach rate increased 8% this week
2 hrs ago
REPORT DAMAC Q4 SLA compliance report generated - sent to Rashid Al-Habtoor
3 hrs ago
Agent Journey - Fraud Detection Pipeline
0 of 5 steps
1
Data Collection
Pending
Scan all tickets with "Waiting for User/Approver" status changes and retrieve communication logs and support engineer activity data.
0 Tickets
0 Pause Events
2
Pattern Analysis
Pending
Apply fraud detection rules to identify suspicious patterns including missing communications, near-breach pauses, and placeholder comments.
0 Rules Hit
0 No Comms
3
Risk Scoring
Pending
Calculate suspicion score based on weighted fraud indicators, support engineer history, and ML-based confidence scoring.
0% Avg Score
0 High Risk
4
Flag & Alert
Pending
Flag tickets with score > 40% as suspicious, generate investigation reports, and calculate SLA accuracy impact.
0 Flagged
0h Sus. Hours
5
AI Deep Analysis
Pending
SCIKIQ GenAI analyzes each flagged ticket to provide detailed reasoning, evidence assessment, and actionable recommendations.
0 Insights
0 Support Engineers
Fraud Detection Algorithm Weights
No Outbound Communication
35%
Near-Breach Timer Pause
30%
Generic/Placeholder Comments
20%
No User Response Evidence
15%
Agent Activity Log
0 events
Waiting to start journey...
Real-time Metrics
-
Flagged
-
Sus. Hours
-
Hidden SLAs
-
SLA Impact
-
Flagged Tickets
-h
Suspicious Pause Time
-
Hidden Breaches
-%
SLA Accuracy Impact
Actionable Insights: Flagged Tickets & Root Causes

Review which tickets the AI flagged, understand why each was flagged using the detection rules below, and see the risk score breakdown. Click into any ticket to view the complete investigation report with specific data points and recommendations.

Export Flagged
Download to Excel
Create Tasks
ServiceNow Tickets
Generate Report
PDF Export
Configure Alerts
Slack & Email

Agent Flagged Tickets

Detection Rules

No Communication
Status changed without email to user
Weight: 35%
Near-Breach Pause
Timer paused within 2h of SLA breach
Weight: 30%
Generic Comments
Placeholder text without specifics
Weight: 20%
No User Response
Extended wait without reply evidence
Weight: 15%
DETECTION TYPES
Fraud
Workload
Bottleneck
Assignment

Flagged Support Engineers

How to Read This Page — Legend & Definitions

Risk Score
80-100% Critical - Likely fraud
50-79% High - Investigate
20-49% Medium - Monitor
0-19% Low - No action needed
Key Metrics
Flagged Tickets: Count of suspicious SLA tickets
Suspicious Pause Time: Hours paused near SLA deadlines
Hidden Breaches: Breaches hidden by AI-detected fraud
SLA Impact: % change in true vs reported compliance
Detection Types
Fraud: Clock manipulation & gamed responses
Workload: Engineer overload & capacity issues
Bottleneck: Category delays & process issues
Assignment: Misrouted tickets & skill gaps
Quick Start
1. Review the hero metrics for quick overview
2. Explore algorithms to understand detection logic
3. Check flagged tickets sorted by risk
4. Take action using quick action buttons
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