AI-Driven Trend Detection
and Insight Extraction for OEM

The Challenge 

The OEM’s support and service organizations were managing massive amounts of unstructured data, including service tickets, technician notes, email communications, and field reports. Despite the data potential, it was not being leveraged effectively due to several challenges:

• Data scattered across systems (CRM, service management, email, and ERP)
• Manual and inconsistent categorization of support cases (root cause, component, region, customer)
• Reactive issue handling – recurring issues were recognized only after customer escalation
• Lack of central visibility into trends such as recurring fault types, component issues, or process delays
• Limited ability to quantify service performance or identify systemic weaknesses across regions

Ultimately, the company sought to move from manual reporting toward a data-driven, trend-oriented understanding of customer support operations and product performance.

The Solution

Scalefocus’ cross-functional team built a data and AI analytics platform focused on consolidating support data and applying AI-driven text analysis, trend discovery, and process intelligence techniques.

Data Integration and Preparation

The team extracted service tickets, email logs, and technician reports from the Salesforce Service Cloud CRM and ServiceNow Service Management. We also ingested data into a unified data lake with automated ETL pipelines and applied data normalization and deduplication to unify identifiers like equipment, product family, serial number, customer, root cause, etc.

Natural Language Processing and Classification

Scalefocus used NLP models such as BERT and domain-tuned language models to analyze free-text fields with ticket descriptions, notes, and resolutions. The solution automatically classified each ticket by product family / system, root cause category, and severity and impact. Topic modelling was also implemented to uncover hidden issue clusters, e.g., sensor calibration drift or firmware update rollback errors.

Trend and Correlation Analysis

Scalefocus built trend detection algorithms to track the frequency and severity of certain issue categories over time. The team also correlated trends with equipment production batch, software version, and geographical region, and developed process intelligence dashboards that visualize resolution time, escalation chains, and communication loops to monitor support team performance.

Generative AI for Insight Summaries

Scalefocus integrated a Generative AI layer to automatically produce monthly Support Trend Reports, summarizing top recurring issue categories, top root causes, emerging customer pain points, and regional service performance insights. The GenAI assistant also utilized text-to-SQL on structured analytics data to draft reports and highlight anomalies in plain English.

Example Insight Generated:

“Service tickets related to actuator control firmware have increased by 18% in the past 2 months — primarily from the Asia-Pacific region and associated with version 3.2. Early firmware revision recommended.”

The Results 

Our team established continuous testing practices and ran automated tests throughout the development lifecycle to obtain immediate feedback on business risks such as delivery delays and defect leakages. The newly launched QA service minimized bugs to enhance the app quality and stability so it could be utilized by its multiple users with their customizations.

• 60% faster trend identification compared to manual reporting cycles
• Early detection of a recurring actuator control issue affecting multiple customers, enabling a proactive engineering fix
• Reduced manual classification time by 70% via NLP-driven ticket tagging
• Improved visibility across regions, allowing leadership to compare performance and failure trends globally
• Automated monthly insights generation reduced reporting workload and improved communication between support, reliability, and product engineering
• Established a data foundation for future predictive maintenance and service optimization initiatives

Strategic Value

This initiative shifted the OEM’s support operations from reactive case management to data-driven reliability intelligence. The client now uses support data not only to fix issues faster but to learn systematically from the field, feed insights back into product design, and strengthen the customer relationship loop.