Executive Summary
Client, a global leader in data platform automation for DevOps and data compliance, engaged RaSee Consulting Group to transform its global support operation through AI-driven case routing and predictive escalation prevention.
Facing rising complexity across its multi-cloud product portfolio, Client sought to improve routing accuracy, reduce case escalations, and ensure high-quality customer experiences for its enterprise clients.
Over a six-month engagement, RaSee designed and implemented a predictive AI routing and sentiment intelligence framework that empowered Client to move from reactive escalation management to proactive, risk-aware support.
Key results achieved:
• 36% reduction in escalations within 90 days
• 22% improvement in routing accuracy
• 59% faster case-to-agent assignment time
• CSAT increase from 4.3 → 4.7
• Early risk detection coverage for 85% of all enterprise cases
The initiative became a benchmark in predictive support — blending AI, process discipline, and human expertise to deliver measurable impact across customer experience and operational efficiency.
Client Context and Challenge
Client provides a data platform that automates data delivery, masking, and versioning for DevOps and compliance teams. The company supports more than 500 enterprise customers across financial services, healthcare, and technology sectors.
As adoption grew, support complexity increased exponentially. Customers engaged with Client’s platform across multiple deployment models — on-prem, hybrid, and multi-cloud — leading to a significant increase in both volume and severity of support cases.
Pre-transformation challenges included:
• Manual and inconsistent case routing: Cases were assigned by queue or region, not expertise or priority.
• High escalation frequency: ~22% of cases escalated due to misrouting or missed SLA triggers.
• Limited visibility into risk: No predictive mechanism existed to identify at-risk cases before escalation.
• Reactive customer management: Escalations were addressed only after customer dissatisfaction surfaced.
• Fragmented data: CRM, product telemetry, and sentiment data were siloed across systems.
These challenges led to slower case turnaround times, higher operational costs, and increasing pressure on escalation engineers. Client’s leadership sought a predictive, AI-driven routing model to improve customer experience while reducing overhead.
Diagnostic and Root Cause Analysis
RaSee began with a quantitative and qualitative diagnostic covering case data, routing logic, and escalation behavior across a 12-month period.
Key findings:
1. Routing accuracy at only 68% — nearly one in three cases reached the wrong team first.
2. Average time-to-assignment: 22 minutes after case creation, adding significant idle time.
3. Escalation triggers: 72% SLA-related, 18% sentiment-related, and 10% product or data gaps.
4. Lack of real-time visibility: Support managers relied on post-incident reviews to identify problem patterns.
5. Data silos: Product telemetry, CRM, and sentiment analytics were not integrated.
The diagnostic confirmed that the absence of intelligent routing and predictive analytics was the primary driver behind Client’s escalation rate and SLA risk exposure.
RaSee’s Predictive AI Routing & Escalation Framework
RaSee designed an AI-enabled routing and risk management architecture tailored to Client’s global support structure. The framework integrated machine learning, natural language processing, and real-time telemetry into a cohesive operational system.
1. AI-Powered Case Intake & Classification:
• Integrated SupportLogic and Salesforce APIs to classify cases by product, sentiment, and urgency.
• Used NLP to extract key phrases from ticket descriptions.
• Trained ML models on 250K+ cases to predict case type and potential risk.
2. Intelligent Routing Engine:
• Replaced region-based queues with skill- and workload-based routing.
• Incorporated real-time availability and performance data.
• Built automated escalation triggers for critical clients.
3. Predictive Escalation Prevention:
• Developed a Case Risk Score that calculated escalation likelihood using sentiment analysis, SLA progress, and metadata.
• Implemented AI-driven alerts for team leads before escalation.
• Enabled proactive outreach workflows for Customer Success Managers.
4. Unified Data & Insight Layer:
• Consolidated CRM, SupportLogic, and telemetry data into a single analytics layer.
• Introduced real-time reporting dashboards.
• Established weekly AI Model Review Sessions between RaSee and Client’s Data Science team.
Implementation and Change Management
RaSee’s implementation followed its structured AI-Transformation Lifecycle™:
1. Foundation Setup (Weeks 1–4): Integrated Salesforce, Splunk, and SupportLogic data. Defined routing attributes and skill matrices.
2. AI Model Deployment (Weeks 5–10): Trained routing and risk models; validated against manual benchmarks.
3. Pilot & Feedback (Weeks 11–16): Piloted in North America and EMEA; collected 1,200+ feedback loops.
4. Global Rollout (Weeks 17–24): Deployed globally with model accuracy >90%; established AI Governance Committee.
People Enablement:
• Introduced AI Routing Analysts to monitor model performance.
• Trained agents on AI Assist dashboards with contextual summaries.
• Provided managers with proactive risk dashboards to drive preventive support.
Outcomes and Quantified Impact
Within six months of full rollout, Client realized significant operational and experiential gains:
Escalations: 22% → 14% (↓36%)
Routing Accuracy: 68% → 90% (↑22 pts)
Time-to-Assignment: 22 min → 9 min (↓59%)
SLA Attainment: 81% → 94% (↑13 pts)
CSAT: 4.3 → 4.7 (↑0.4)
Qualitative Outcomes:
• Proactive intervention eliminated 60% of avoidable escalations.
• Sentiment-driven triage enabled early resolution for enterprise clients.
• Risk alerts issued 4.5 hours before potential SLA breaches.
• AI accuracy sustained above 92% after three retraining cycles.
Visual Placeholders
- Figure 1: AI-Driven Case Routing Framework – From Case Intake to Predictive Escalation Prevention.
2. Figure 2: Before vs After Support Flow – Reactive vs Predictive Routing Model.
3. Figure 3: Performance Dashboard – Escalations, Routing Accuracy, and SLA Trends.
Lessons Learned & Best Practices
- AI thrives on feedback loops: Real-time agent validation was critical for sustained accuracy.
• Cross-functional ownership matters: Collaboration among Support, Data Science, and Success teams enabled faster refinement.
• Visibility drives confidence: Dashboards built trust across leadership.
• Proactive beats reactive: Predicting escalation risk proved more valuable than managing escalations.
Conclusion
RaSee Consulting’s engagement with Client redefined intelligent support. By combining AI, predictive analytics, and structured governance, RaSee enabled Client to evolve from reactive case handling to proactive customer assurance.
The outcome: fewer escalations, faster routing, higher CSAT, and a cultural shift — support teams became strategic partners in customer retention and product reliability.
Client’s AI-driven case routing framework now serves as a model for other enterprise support organizations seeking to balance scale, precision, and customer excellence.