Data Cosmos Ticket Analyzer – RAG Powered Support Ticket Intelligence
Coforge Limited · Cybersecurity & IT
Certification per AWS Marketplace.
Evidence tier Source Confirmed · 4 captures on record
What the publisher says
As described on AWS Marketplace.
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
Show the rest of the publisher’s description (30 more lines)
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:
- 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.
- 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.
- Contextual Resolution Engine
Surfaces past resolutions, root causes, and recommendations instantly. Finds historically similar tickets and presents actionable resolution steps.
- Pattern & Trend Analyzer
Identifies recurring issues, failure patterns, and bottlenecks across the entire ticket corpus. Proactively highlights systemic problems before escalation.
- 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.
Highlights
Highlighted by the publisher on AWS Marketplace.
RAG-powered conversational AI for natural language ticket exploration and resolution discovery
30–50% faster issue resolution and 40%+ knowledge reuse from historical ticket intelligence
Pattern and trend analysis identifying recurring issues and operational bottlenecks proactively
Agent build and provenance
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Compliance
- FedRAMPConfirmedNot listed90%, registry-checkedNo FedRAMP Marketplace entry matched this vendor's domain, checked 2026-08-27registry recordas observed 2026-08-27
Confirmed means matched to a public authoritative registry. Claimed means the vendor or its listing states it, not yet cross-checked. A framework not shown was not found in any source we hold, which is not evidence against it. Not listed means a scoped registry check found no match for this vendor's domain: a No is a scoped registry check, not a compliance judgment. Confidence bands: 95% domain-verified, 90% registry-checked, 80% self-attested, 70% weak signal. Self-attested items marked “vendor's site” are gathered from the vendor's own website and are not verified by us.
Vendor
External enrichment · as of 2026-08-29
Sources
Linked repositories
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