Field Management Agents Accelerator: AI-Powered Knowledge Platform for Enhanced Field Service Efficiency

The Matoffo team developed an AI-powered field service knowledge platform for a global digital business and technology transformation company to address knowledge access, service efficiency, and customer satisfaction challenges. Leveraging a robust tech stack, our talented engineers created a tailor-made solution that enables real-time retrieval of technical manuals, context-aware troubleshooting assistance, and multimodal data support. Built on robust AWS infrastructure, the solution can support thousands of field agents, resulting in significant cost savings and better customer experiences.

Our client is a global digital business and technology transformation company. Their primary mission lies in helping enterprises achieve agility and accelerate growth by delivering innovative solutions. Partnering with multiple organizations across the United States and Europe, the company serves various industrial verticals, from finance and healthcare to retail and eCommerce. They specialize in digital product engineering, data and analytics, DevOps, and AI-driven solutions. While business digitalization is their main objective, their team leverages the full potential of data, cloud, and technology platforms to drive visible business outcomes, enhance customer experiences, and streamline operations.

In order to scale their growth, our client experienced an urgent need to solve several challenges related to efficiency, accuracy, and productivity. Let us take a precise look at each of them.

First and foremost, the company faced issues with knowledge access and retrieval since there was no ability to access and navigate complex technical manuals and troubleshooting guides in real time, leading to delays in service resolution. On the other hand, it is essential to mention that information overload makes it challenging to find the right information quickly.

Besides, the varied complexity of service tasks also affected the company’s performance. Agents performed 6 to 8 service routines of differing complexity per day, lacking real-time guidance for efficient task completion. To sum up, the above mentioned challenges significantly impacted the client’s ability to deliver timely and effective services, causing low customer satisfaction rates and bad operational efficiency.

Specializing in AWS cloud solutions, DevOps, and data engineering services, the Matoffo team is an official AWS Advanced Tier Services Partner trusted by various clients from all over the world. Adept at delivering best-in-class cloud, container management, data analytics, and generative AI solutions, we came up with a detailed project concept that addresses all client’s pain points in a professional manner. Hence, our client entrusted this solution to our skilled engineers. 

AWS Connect and AWS Chime provide reliable and scalable voice and video communication solutions. Their integration ensures seamless connectivity between field agents and the central platform. Over and above that, AWS excels in AI and machine learning (ML) services, making it an ideal option for implementing advanced Large Language Models (LLMs). Additionally, AWS is also famous for its excellent analytics capabilities, which means it is a great technology foundation for building a solution that needs to extract meaningful insights from the data.

In a nutshell, AWS’s vast range of services, combined with our strong capabilities in data processing, AI, and real-time communication, makes it the perfect choice for developing a cutting-edge service solution.

To implement a robust AI-powered Field Service Knowledge Platform, our engineers leveraged advanced Large Language Models (LLMs) like Claude or GPT to assist agents in real time. To start with, we created a highly available and scalable architecture, including the following:

1. Application Layer:
   • iOS App for voice and video communication;
   • AWS Connect and AWS Chime for seamless communication;
   • WebSocket (AppSync) for real-time updates.
2. AI Abstraction:
   • LLM Abstraction and Workflow on EKS;
   • RAG Inference using AWS Bedrock;
   • Integration with Claude 3.5 via Bedrock and GPT-4.
3. Data Processing and Storage:
   • AWS Kinesis Video Streams for call ingestion;
   • AWS Lambda and Step Functions for call processing and summarization;
   • AWS DynamoDB for workflow state management;
   • S3 buckets for Raw, Refined, and Publish zones in the Enterprise Data Lake.
4. AI and Analytics:
   • AWS Sagemaker for metadata extraction and semantic search index pipeline;
   • AWS Comprehend for sentiment analysis;
   • AWS Quicksight for conversation memory and analytics.
5. Integration and Communication:
   • Enterprise ETL Layer with Fivetran and Kafka;
   • AWS Pinpoint for personalized messages and emails.

As we moved through the development process, our main objective was to enable agents to retrieve relevant sections of technical manuals and troubleshooting steps using natural language queries.

Moreover, we were striving to ensure agents can conduct real-time consultations, providing context-aware responses for complex problem-solving and, in turn, reducing the need for expert intervention.

Speaking of scalability, we built our solution on cloud-native services to efficiently scale from supporting 1,000 to 10,000 field agents. Finally, the solution integrates with OCR tools (like AWS Textract) to analyze physical documents and technical drawings, ensuring multimodal data support is in place.

To complete this solution, we used the following tech stack: iOS, AWS Connect, AWS Chime, WebSocket (AWS AppSync), AWS Bedrock, Claude 3.5, GPT-4, AWS Kinesis, AWS Lambda, AWS DynamoDB, Amazon S3, AWS SageMaker, AWS Comprehend, AWS QuickSight, Fivetran, Kafka, AWS Pinpoint. The development team included an ML consultant, Business analyst, MLOps/LLM engineer and DevOps engineer. It took us 6 weeks to deliver a ready-to-use AWS solution.

What can be better than delving into the intricacies of the development process? Well, the answer is simple: measuring the business value. Here are 5 measurable benefits our client gained due to Matoffo’s solution:

• Cost Efficiency

With our solution, the company saves 1-2 hours per agent per day through reduced search time, improved first-time fix rates, and reduced expert dependency.

• Efficiency Boost

Thanks to significant improvement in service capacity (15-25%), each agent can handle more daily service calls.

• Improved First-Time Fix Rate

The solution resulted in 20-30% first-time fix rate improvement, reducing the need for follow-up visits and enhancing overall customer satisfaction.

• Excellent Customer Experience

The company enjoys high client satisfaction rates caused by a 30-40% reduction in service resolution times.

• Grow and Scale Easily

The cloud-native architecture ensures seamless scaling to support a growing number of field agents without considerable infrastructure changes.

The Matoffo team conducted a seamless AWS integration, ensuring the platform’s scalability exceeded the client’s expectations. With such a robust solution, the company can grow without worrying about infrastructure constraints. In general, our solution resulted in improved efficiency, enhanced first-time fix rates, and better customer experience, so the client was delighted with the result. Summing up, Matoffo would be excited to continue this fruitful cooperation with such an incredible client.