Support Ticket Response Automation Solution using both Generative and Predictive AI

Use Large Language Model (LLM) to provide quality response rapidly

Faster, better response to end users

End users get accelerated response for common requests and higher quality support for complex requests.

Reduced human time spent for first-line support and final resolution

IT support teams save time on repetitive tasks, enabling them to focus on complex and high-priority tasks.

Reliable GenAI response with LLM monitoring and guardrail

With LLM monitoring metrics and guardrail models, executive teams and line of business leaders can trust DataRobot’s GenAI solutions.

How it Works

By combining predictive and generative AI, Evolutio creates AI-powered applications with chat interfaces allowing both end users and IT support teams to streamline support ticket creation, categorization, prioritization, and response.

First, IT support tickets are clustered and labeled with categories—issues related to operating systems, VPN, network, hardware, etc. Additionally, machine learning models predict ticket priority, ensuring each new ticket is both clearly categorized and prioritized.

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Customer clustering and categorization

Next, historical IT support responses and documentations are used to create a vector database with a text embedding model. Relevant information from these proprietary documents can now be retrieved based on customer query and used as additional context for the LLM to generate responses both to end users and the IT support team.

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GenAI solution interacts with both end user and support deck admin

Key Deliverables

  • End-to-end generative + predictive AI solution
  • Frontend applications to provide Generative AI response for both end users and IT support team, with user feedback captured
  • Predictive AI model for IT ticket clustering, categorization, and prioritization
  • Retrieval augmentation generative workflow for high quality response, with customer’s choice of embedding model, vector database, and LLM
  • Documents data and tabular data preparation for both generative and predictive AI
  • LLM observability in production with custom metrics to monitor LLM performance
  • Implementation of LLM guardrails to block unwanted prompt and LLM response

About the Partner

Evolutio specializes in helping organizations address the operational challenges of building and scaling complex enterprise applications. The professional service team deploys and optimizes proven technologies to increase revenue, enhance brand loyalty, and provide a premium digital experience.

With extensive industry experience in custom development, data engineering, analytics, machine learning, and MLOps, Evolutio offers proven enterprise project management experience, as well as technical certifications with DataRobot, AWS, GCP, Azure, Snowflake, SAP, Cisco.

Evolutio Data Science services are tailored to accelerate clients’ AI maturity, from first machine learning use case to the implementation of an enterprise AI Center of Excellence. Their expert team supports the full AI lifecycle, including enterprise ML experimentation, deployment and custom application development. Client success stories include Propensity to Buy, Supply Chain Time Series Forecasting, Visual AI, GenAI Solutions, NLP, Employee and Client Retention, and Edge Compute Industrial ML Applications, etc.

Solution Diagram

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Retrieve Augmented Generative solution for IT support

Success Story

Evolutio has developed the support ticket response automation solution for an international manufacturer. The solution generates rapid and high quality automatic responses for their IT support team and the end customer user. With the solution, the customer is able to reduce labor and time to respond to repetitive IT support tickets. With saved time, the IT support team is able to focus their bandwidth on high priority and complex customer requests. The business result is improved customer approval rating for faster and higher quality IT support, with improved support team work satisfaction.

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