Ticket Analyzer – RAG-Powered Support Ticket Intelligence Engine

Overview: Ticket Analyzer is an AI-powered support ticket intelligence platform that transforms reactive ticket handling into proactive, intelligence-driven support operations. By combining LLMs with enterprise ticket data through RAG, it retrieves relevant historical tickets and generates context-aware responses — enabling teams to find resolutions faster, identify patterns, and reduce SME dependency. Part of Coforge Data Cosmos™ - the innovation backbone comprising of platforms, agents, and services that accelerates execution across every phase of the data lifecycle

Client Challenges: • Limited Discoverability — Difficulty finding relevant past tickets • Knowledge Silos — Poor visibility into historical resolutions • Manual Effort — Time-intensive analysis of large ticket volumes • SME Dependency — Repeated queries requiring expert intervention • No Intelligent Query Layer — Lack of conversational ticket access

Core Capabilities:

1. RAG-Powered Intelligence Engine Combines LLMs with real-time retrieval of historical ticket data. Answers are grounded in actual enterprise ticket history — not hallucinated. Retrieves semantically similar tickets and provides cited, traceable responses.

2. Conversational AI Interface Natural language interaction for ticket exploration. Ask: “What are common failures in pipeline X?” or “How was issue Y resolved?” No SQL required.

3. Contextual Resolution Engine Surfaces past resolutions, root causes, and recommendations instantly. Finds historically similar tickets and presents actionable resolution steps.

4. Pattern & Trend Analyzer Identifies recurring issues, failure patterns, and bottlenecks across the entire ticket corpus. Proactively highlights systemic problems before escalation.

5. Real-Time Analytics Layer Instant insights on resolution rates, open vs. resolved ratios, category distributions, SLA compliance, and team performance — all via natural language.

Industry Applications: • Banking — Analyze 50,000+ tickets across core banking, risk, and regulatory systems. Identify recurring ETL failure patterns and surface proven resolutions in seconds. • Insurance — Ticket intelligence across claims, policy admin, and billing. Pattern analyzer identifies seasonal bottlenecks and recommends capacity adjustments. • Travel — Analyze booking system and GDS integration tickets. Identify peak-season failure patterns and surface resolutions for recurring API timeout issues. • Healthcare — Clinical system support ticket analysis with HIPAA-compliant data handling. Identify recurring EMR interface failures and HL7/FHIR integration issues.

Expected Outcomes: • 30–50% faster issue resolution through AI-driven insights • 40%+ knowledge reuse leveraging historical ticket intelligence • 25–35% efficiency gain for support and engineering teams • Reduced SME dependency via RAG-powered self-service intelligence • Enhanced visibility into trends, patterns, and operational metrics

Cloud-Native Deployment on AWS: Deployed on Amazon EKS. Amazon Bedrock provides LLM reasoning. Amazon OpenSearch Service enables semantic retrieval. Amazon S3 stores ticket corpus. Integrates with JIRA, ServiceNow, and BMC for ticket ingestion.