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Business Overview

The client, a global technology provider of consumer and enterprise hardware, operates a cloud-based platform for device fleet management. Their complex enterprise ecosystem, coupled with a low request processing time stemming from a limited number of operators, created a steep learning curve for IT Admin staff.

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

This innovative solution aimed to streamline operations by enabling the system to execute tasks through natural language prompts, alongside the generation of tailored recommendations and suggestions based on user context and relevance.

Leveraging natural language processing (NLP) capabilities, the platform would enable seamless interaction via chat, allowing users to articulate their needs in a conversational manner and receive insightі relevant visualizations.

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 developed and deployed a sophisticated AI Agent on AWS that significantly boosts operational efficiency and improves decision-making. Built with Python/FastAPI on EC2 and orchestrated using LangGraph, this solution seamlessly integrates with existing business tools. We leveraged various AWS Bedrock LLMs (including Claude 3.5 Sonnet and Mistral) for intelligent interactions. A custom React UI provides data visualization and a crucial human feedback mechanism, continuously refining system performance.

A core component is a high-performance RAG system powered by PostgreSQL/PGVector, ensuring rapid and accurate data-driven insights. This deployment delivers tangible business value through enhanced process automation and improved knowledge accessibility. We ensured traceability with LangFuse logging and optimized prompt quality for better suggestions using DSPy.

Enterprise Device Management

Our robust, scalable AI Agent is a strategic asset, directly supporting business goals and providing a clear competitive edge within AWS. Our selected tech stack enabled seamless integration and significantly reduced processing times for critical on-demand visualizations and intelligent, context-aware suggestions, resulting in excellent client ROI and high satisfaction.

AI Assistant for Enterprise-grade Device Management

The solution includes the following deliverables:

  • Chat Manager is a main AI Agent module that serves as the user interface for natural language interactions, relays prompts to the LLM, and identifies actionable instructions.
  • LLM, at the core of an AI agent, processes natural language, understands context, generates responses, and identifies actionable queries for execution.
  • Action Executor, connected via LangGraph, 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 AI agent 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 agent 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 Cloud, AWS Bedrock, Python, LangChain, OpenAI GPT, Anthropic Claude, Pinecone, Java, ReactJS, Titan, Docker, PostgreSQL, Kafka, Airflow, GPTCache, MLflow, PyTorch, HTML, CSS, Apache ECharts, AWS ECS*, Route53, WAF, CloudFront, Lambda, ALB, RDS, S3, Amazon MemoryDB for Redis, Amazon OpenSearch Serverless, PGvector, React, LangGraph, LangFuse, FastAPI, DSPy

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