Client Context
A global provider of mission-critical flow creation and industrial solutions that operates a globally distributed IT landscape with:
- Multiple SAP systems
- Oracle E-Business Suite
- Several enterprise applications supporting regional operations
The scale, regional diversity, and 24×7 operational requirements introduced challenges across both L1 and L2, with knowledge fragmentation amplifying the problem.
Operational Challenges
L1 Challenges – Context, Scale & Language
- Multiple SAP systems across regions made it difficult for L1 teams to:
- Understand application and business context
- Identify the correct resolver group
- Manual triage led to:
- High response and transfer times
- Inconsistent routing accuracy
- Dependency on language-specific helpdesk staff due to global ticket intake
L2 Challenges – Volume, MTTR & Intelligence
- High volume of SAP & Oracle tickets requiring L2 expertise
- MTTR impacted by:
- Misrouted tickets
- Incomplete context reaching L2 teams
- Repeated issues handled repeatedly with:
- Limited intelligence on patterns
- No systematic automation discovery
- Bandwidth constraints for:
- RCA
- Round-the-clock coverage
- Continuous improvement initiatives
Knowledge Challenges – Tacit & Fragmented
- Critical knowledge locked in:
- Individual experience
- Long-tenured SMEs
- New joiners faced:
- Long onboarding cycles
- Steep learning curves
- Business users lacked visibility into:
- Application landscape
- Business processes
- Project goals and implementations
Qinfinite Solution – AI Across the Ticket Lifecycle
Qinfinite was implemented as an AI-augmentation layer spanning L1, L2, and Knowledge Management, with the goal of reducing MTTR, improving scale, and democratizing intelligence.
Solution Pillars & Outcomes
Intelligent L1 Triage (Enabler for L2 Efficiency)
Qinfinite introduced AI-driven triage for SAP & Oracle tickets.
Capabilities
- Context-aware classification across multiple SAP systems
- Language-agnostic understanding (German, Spanish, Italian, Chinese, English)
- Consistent routing using historical patterns and semantic understanding
Measured Outcomes
- L1 response + transfer time: ~4 hours → < 1 minute
- Assignment accuracy: ~75% → ~90%
- Reduced dependency on language-specific L1 resources
This significantly reduced noise and delays reaching L2 teams.
AI-Driven L2 Automation & MTTR Reduction
As part of onboarding, Qinfinite analyzed ~18 months of ServiceNow tickets using AI-powered cluster analysis.
What Qinfinite Did
- Identified:
- Top repeating tickets per support group
- High-impact automation candidates
- Validated use cases with SMEs
- Automated top 5 L2 scenarios end-to-end
Measured Outcomes
- MTTR reduced: ~12 hours → ~5 minutes
- End-to-end automation from ticket creation to resolution
- Significant reduction in L2 workload and bandwidth pressure
- 24×7 resolution without proportional staffing increases
Knowledge Agent – Democratizing Enterprise Intelligence
Qinfinite’s Knowledge Agent converted:
- Historical tickets
- KBs
- Application metadata
- Project documentation
into conversational, searchable intelligence.
Benefits
- Faster onboarding for new L1 and L2 engineers
- Reduced dependency on senior SMEs
- Business users gained clarity on:
- Business processes
- Application dependencies
- Project goals and implementations
Strategic Impact for the Client
- Balanced optimization across L1, L2, and Knowledge
- Reduced MTTR without adding headcount
- Scaled global operations with consistent quality
- Converted operational experience into reusable, AI-driven services
Executive Positioning Statement
“Qinfinite helped the company modernize SAP and Oracle EBS operations by combining intelligent triage, L2 automation, and knowledge democratization—reducing resolution times from hours to minutes while enabling global scale.”
