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Business Overview

The client is a global technology provider of consumer and enterprise hardware operating a cloud-based platform for device fleet management. Their complex enterprise ecosystem created a steep learning curve for IT Admin staff.

The client sought NIX’s expertise to design a new platform that will provide an AI-powered virtual assistant and sophisticated data visualization tools to facilitate in-depth analytics.

This innovative solution would leverage natural language processing (NLP) capabilities to enable seamless interaction via chat, allowing users to articulate their needs in a conversational manner and receive insightful, contextually relevant responses.

Project Scope

The NIX team’s scope included the following deliverables, utilizing generative AI:

  • 1

    An intelligent assistant and in-flight data visualization tools

  • 2

    AI-based task automation, context-aware suggestions

  • 3

    Capability to scale across the various vertical solutions and evolve over time

Solution

The NIX team engineered a sophisticated solution by harnessing the power of AI and integrating both system data and user feedback into a cohesive framework. This approach involved utilizing AI to translate natural language queries into executable instructions, which were subsequently validated and executed by core system services, ensuring both accuracy and efficiency.

Leveraging AWS Bedrock and Anthropic Claude models, we designed an integrated system with a user-friendly chat interface. This system enables users to gain actionable insights and trigger API actions directly through natural language interactions. To ensure smooth and efficient operation, we incorporated a robust state machine, which acts as a central coordinator, guiding the system through different stages of tasks and ensuring they are executed in the correct order.

Enterprise Device Management

Furthermore, NIX data scientists augmented the system’s data comprehension by integrating a retrieval-augmented generation (RAG) knowledge base. This, combined with decision and recommendation engines, bolsters intelligent decision-making capabilities. The implementation of intricate, multi-step workflows with conditional logic ensures the system’s adaptability and robustness in navigating complex, real-world scenarios.

AI Assistant for Enterprise-grade Device Management

The solution includes the following deliverables:

  • Chat Manager serves as the user interface enabling natural language interactions, which translates user requests into actionable prompts for the LLM.
  • RAG integration to augment the system’s data comprehension and responsiveness to queries by incorporating relevant external data.
  • Integration of LLM that processes natural language, understands context, generates responses, and identifies actionable queries for execution.
  • Action Executor executes LLM instructions and supports complex workflows with multiple steps, scheduling, error handling, and API integration.
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Outcome

By leveraging generative AI for smart assistant and data visualization, the client significantly enhanced IT admin efficiency and productivity through automated problem-solving and reduced resolution times. This led to an improved user experience and increased customer satisfaction while lowering support costs. Additionally, the platform provides valuable data insights to support better decision-making and optimize resource allocation.

 

To ensure a long-term market lead for the solution, NIX experts provided a roadmap for the feature enhancements and new features, including device care recommendations and a feedback loop. Ultimately, this AI-powered solution for enterprise-grade device management positions the client as a leader in the industry, driving competitive advantage and market growth.

Key Outcomes:

30%

reduction in average ticket resolution time

25%

improvement in device uptime

40%

reduction in support costs

Team:

Team:

Project Manager, 2 QA Engineers, 3 Data Scientists
Tech Stack:

Tech Stack:

AWS Bedrock, Langchain, Anthropic Claude, Pinecone, Java, ReactJS, Python, Titan, Docker, PostgreSQL, Kafka, Airflow, GPTCache, MLflow, PyTorch, Transformers, AWS ECS, CloudFront, Lambda, RDS, S3

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