Coforge's Data Cosmos Ticket Analyzer

Ticket Analyzer is Coforge's AI-powered support ticket intelligence platform, part of the Coforge Data Cosmos™ toolkit, that transforms reactive ticket handling into proactive, intelligence-driven support operations on Azure. By combining LLMs with enterprise ticket data through Retrieval-Augmented Generation (RAG), it retrieves relevant historical tickets and generates context-aware responses — enabling teams to find resolutions faster, identify patterns, and reduce SME dependency.

Client Challenges: • Limited Discoverability — Difficulty finding relevant past tickets across fragmented ITSM systems • Knowledge Silos — Poor visibility into historical resolutions and institutional knowledge • Manual Effort — Time-intensive analysis of large ticket volumes consuming engineering bandwidth • SME Dependency — Repeated queries requiring expert intervention for common issues • No Intelligent Query Layer — Lack of conversational, AI-driven 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.

Key Use Cases: • 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.

The 8-week implementation engagement covers: discovery and ticket data profiling, platform deployment on Azure, ticket corpus ingestion and vectorization, RAG pipeline configuration, conversational AI interface setup, pattern analyzer activation, analytics dashboard configuration, and knowledge transfer.

Target Audience: Enterprise IT Support & Operations Teams, AMS Delivery Leads, Service Desk Managers, Data Engineering Teams, and IT Operations Leaders.