About Client
A cloud-native IT Service Management (ITSM) product company provides solutions that help enterprises manage incident tickets and service requests at scale. As part of its platform evolution, the company set out to integrate Generative AI capabilities to enable intelligent ticket summarization, similar ticket discovery, automated resolution guidance using historical incidents, and natural language based analytics. The objective was to improve decision-making, accelerate resolution times, and enhance technician productivity.
The ITSM platform set out to evolve beyond manual ticket triage and basic automation toward a more intelligent, AI-driven operating model. This required building a scalable and secure system capable of supporting automated incident classification alongside a conversational AI chatbot. The solution needed to handle large volumes of historical and real-time ticket data while enabling natural language based ticket summarization, similar ticket discovery, and analytics. Key challenges also included integrating AI models with existing data systems such as object storage, search, and analytical data warehouses; maintaining conversational context across multi-turn chatbot interactions; ensuring response accuracy and relevance; and delivering actionable insights for both technicians and end users. In parallel, the platform needed strong monitoring, logging, and error-handling mechanisms that aligned with enterprise security and compliance standards.
To enable intelligent ticket management, Comprinno implemented a Generative AI–powered ITSM chatbot that combines advanced reasoning with natural language interaction. The solution uses Amazon Bedrock Agent (Nova Pro) as the central intelligence layer to process, summarize, and analyze ticket data, delivering contextual insights and similar ticket discovery. A fully serverless architecture was built using AWS Lambda, Amazon S3, Amazon Redshift, AWS Glue, and an Application Load Balancer (ALB) to handle ticket ingestion, data processing, and real-time query flows between users and the AI model. The chatbot interacts with two knowledge bases: a structured Redshift-backed knowledge base for analytical queries and an unstructured S3-based vector store for historical ticket content. This design enables seamless reasoning across both structured and unstructured data. Lambda functions orchestrate data movement and AI workflows, executing ticket summarization, similarity searches, and analytics, while the ALB exposes low-latency, session-aware endpoints to support responsive, conversational interactions at scale.
Key Actions:
Bedrock Agent & LLM Integration :
           Serverless Data Pipeline Architecture :
Knowledge Base Configuration :
Lambda Function Orchestration & Workflow Management :
Security & Governance Implementation :
Performance Monitoring, Scalability & Optimization :
Comprinno designed a secure, scalable, and highly available serverless architecture on AWS to support customer requirements for data processing and analytics. The solution leverages Amazon API Gateway as the entry point to handle user requests, which securely invoke AWS Lambda functions deployed across multiple Availability Zones, ensuring high availability, fault tolerance, and automatic scaling.
The Lambda functions interact with Amazon DynamoDB as the primary data store, enabling fast and reliable NoSQL data access for real-time queries and updates. All components are deployed within a Virtual Private Cloud (VPC), with public subnets hosting NAT Gateways to allow secure outbound connectivity for private application subnets.
Security and governance are enforced using AWS IAM for access control, AWS KMS for encryption, Amazon Cognito for identity and authentication, and AWS WAF to protect APIs from common web threats. Continuous security monitoring and compliance visibility are provided through AWS GuardDuty, AWS Security Hub, and AWS CloudTrail.
Operational monitoring and observability are achieved through Amazon CloudWatch for logs and metrics, Amazon OpenSearch for log analytics, and AWS Systems Manager for centralized operational management. Amazon S3 is used for object storage, log archival, and backups, while AWS Glue and Amazon SageMaker support data processing and advanced analytics use cases. This architecture ensures robust security, seamless scalability, and minimal operational overhead, aligning well with cloud-native and data-driven application objectives.
Enhanced User Experience: Context-aware, conversational interactions improve visibility into ticket history and related incidents, supporting multi-turn queries and more intuitive engagement for users.
The implementation of the Generative AI–powered ITSM chatbot transformed incident management by combining AI-driven intelligence with AWS-native services. Built using Amazon Bedrock, AWS Lambda, Amazon Redshift, and S3-based vector stores, the solution delivers real-time, context-aware ticket summarization, similarity search, and advanced analytics. This approach effectively eliminates manual ticket triage, reduces delays in insight generation, and ensures consistent incident handling while maintaining high accuracy and operational efficiency. Beyond ITSM, the architecture establishes a scalable, secure, and modular AI foundation that can be extended to other enterprise use cases such as automated customer support, knowledge management, and predictive analytics. The result is a robust, cost-efficient, and intelligent platform that enhances user experience, boosts technician productivity, and enables sustained operational excellence.
Comprinno Achieves AWS Data and Analytics Competency | Read More →
