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Building a Personalized Recommendation Assistant for Jonard Tools

Client

Jonard Tools

Location

Elmsford NY

Industry

Telecom Tools Manufacturing like CATV, Fiber Optic, Home Automation, Security & Alarm, and Electrical markets.

Services & Tech

AWS Sagemaker, AWS Cloudwatch, AWS Bedrock, Amazon API Gateway, Amazon RDS Tags : Smart recommendation assistant, GenAi, Telecommunications, Data Processing

Project Overview

Jonard Tools, a leader in manufacturing tools for Telecom, CATV, Fiber Optic, Home Automation, Security & Alarm, and Electrical markets, sought to build a personalized recommendation assistant to suggest the right set of tools for any given task to its customers. By leveraging AWS AI services, Jonard Tools aimed to drive product recommendations, automate processes, and streamline customer support using Retrieval-Augmented Generation (RAG) models on AWS Bedrock and SageMaker. Avahi, an advanced tier AWS partner, was engaged to help Jonard Tools on this transformative journey.

The
Problem

Jonard Tools faced several challenges in providing personalized product recommendations and efficient customer support:

  • Personalization: Creating a recommendation system that suggests the right tools based on specific tasks and customer preferences.
  • Automation: Automating the process of product recommendations to reduce manual efforts and increase efficiency.
  • Scalability: Building an infrastructure capable of handling large volumes of data and user queries.
  • Customer Support: Streamlining customer support to quickly and accurately respond to customer queries.

Why AWS

Key Components

1. Data Acquisition and Storage

  • Amazon S3: Used for storing input documents, product data, and other assets. S3’s scalability and durability ensured efficient handling of large volumes of data.
  • Amazon RDS: Provided a scalable and managed database service for storing structured user and product data.

2. Event-Driven Processing

  • AWS Lambda: Deployed to run event-driven functions that process data and manage various stages of the recommendation pipeline. Lambda functions were used to fetch product documents, create and update embeddings, and handle real-time data processing.

3. Machine Learning and AI

  • AWS SageMaker: Utilized for developing, training, and deploying machine learning models. SageMaker was key in creating the recommendation models and handling the intelligent processing of user queries.
  • AWS Bedrock: Employed for scalable managed Generative AI services, ensuring efficient and robust AI model deployment.

4. API Management

  • Amazon API Gateway: Provided a managed service to create, publish, maintain, and secure APIs. This facilitated seamless communication between the front-end applications and backend services.

5. Monitoring and Logging

  • AWS CloudWatch: Implemented for comprehensive monitoring and logging. CloudWatch provided real-time insights into system performance, user interactions, and the health of the deployed models.

Solution Workflow

  • Data Ingestion: Jonard Tools provided input data, including product documents and user preferences, which were ingested into Amazon S3.
  • Data Processing: AWS Lambda functions were triggered to process the ingested data, creating and updating embeddings for the product documents.
  • Model Training and Deployment: Using AWS SageMaker, machine learning models were trained on processed data to generate personalized recommendations.
  • API Access: The trained models and recommendation logic were exposed via APIs managed by Amazon API Gateway, enabling the front-end applications to interact with the backend services.
  • Monitoring and Feedback: AWS CloudWatch monitored the system performance and user interactions, providing valuable insights for continuous improvement.

Solution

Avahi proposed a comprehensive solution architecture leveraging various AWS services to address these challenges. The focus was on developing an end-to-end smart recommendation assistant that could provide personalized recommendations and support to Jonard Tools’ customers.

Explore Our Services

We highly recommend Avahi as a reliable and innovative technology partner. Their expertise in cutting-edge technologies was instrumental in building our Proof of Concept (PoC) and developing our Minimum Viable Product (MVP). Avahi consistently delivered high-quality solutions on time while maintaining a collaborative, responsive approach. They went beyond expectations by identifying opportunities for enhancement, ensuring scalability and compliance for our law enforcement-focused products. Avahi is the clear choice if you need a tech partner with industry knowledge, professionalism, and a commitment to innovation.

Brandon Puhlman

Founder, Bravo Foxtrot

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