With our Generative AI services, you'll enjoy a range of benefits, including cost-effective and scalable capacity for running AI workloads, and support for industry-standard AI frameworks. Our services handle many of the underlying infrastructure management tasks, allowing your team to focus on developing and innovating your AI-driven applications.
Whether you're a small startup or a large enterprise, our team is equipped to tailor the implementation of Generative AI solutions to your needs. We leverage the foundational models available on AWS Bedrock, ensuring a robust architecture and seamless integration. We'll work closely with you to understand your requirements and provide ongoing support to ensure your success in the AWS Cloud.
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This global non-alcoholic beverage bottling company has established retail points across all countries in Latin America. Their extensive distribution network ensures that a diverse range of consumers can access their products efficiently and reliably, demonstrating their commitment to widespread market penetration and customer accessibility.
The client required a tool that would allow their B2B customers to place orders, check shipments, and modify delivery schedules easily and be seamlessly integrated within their existing systems. The need was to enhance operational efficiency and customer satisfaction by automating these processes, which were previously manual and time-consuming.
After a thorough analysis of the user flow and necessary integrations, we developed a chatbot powered by generative artificial intelligence, utilizing AWS Bedrock's natural language processing models. Employing Retrieval-Augmented Generation (RAG) techniques, the chatbot uses the bottler's pricing list and promotions as a knowledge base. Furthermore, it integrates fully with the bottler’s internal order generation system, enabling B2B clients to autonomously place orders 24/7 from any device.
The use of AWS Bedrock foundational models in our chatbot solution offers significant advantages. These models provide a robust framework for understanding and generating human-like text, enhancing the chatbot's ability to interact naturally with users. This capability not only improves user engagement but also allows for more accurate and contextually appropriate responses, streamlining communication and decision-making processes in real-time.
The implementation of this AI-driven solution has dramatically accelerated the sales process and order generation. B2B clients benefit from the ability to place emergency orders in response to stock shortages or increased demand due to special events. This flexibility ensures that they can maintain optimal inventory levels, enhance their responsiveness to market conditions, and improve overall service delivery.
The client's product dataset is uploaded to an Amazon S3 bucket, serving as the initial repository for raw data.
Raw product data is transformed into numerical vectors using an AWS Glue job and Amazon Titan Embeddings model. These vectors are stored in Amazon Aurora PostgreSQL with the pgvector extension to facilitate vector-based similarity searches.
User queries are processed by a Lambda function that converts the input into a numerical vector. A k-nearest neighbors (k-NN) search in Aurora PostgreSQL identifies the most relevant products for the client. Recommendations and shopping cart interactions are managed using Amazon Bedrock and the Claude LLM language model, with data stored in DynamoDB.
The system uses AWS Lambda for scalable user interactions and AWS Glue with serverless Aurora PostgreSQL for data processing scalability. High availability is achieved through the use of managed AWS services across multiple availability zones.
IAM policies ensure secure access within the AWS ecosystem, and Amazon CloudWatch monitors the system’s performance, with alerts configured to maintain operational integrity.