AWS for Industries

How contract manufacturers can increase efficiency with generative AI

Assembling complex products with speed and precision is critical, yet extremely challenging, for contract manufacturers. While these companies play an integral role in the manufacturing value chain across industries from electronics to healthcare, their assemblers face disconnected data and tight deadlines that lead to delays and costly errors. But what if assemblers could instantly access the assembly drawings, instructions, and specifications they need? Innovative technologies like the generative Artificial Intelligence (AI) Retrieval-Augmented Generation (RAG) chatbot application for manufacturing on AWS can help assemblers quickly access and interpret complex data – preventing errors and accelerating assembly times.

In this blog, we’ll explore how generative AI RAG chatbot application (RAG chatbot) on AWS provides contract manufacturers with a powerful new tool to drive efficiency, quality, and competitiveness. By seamlessly enabling assemblers to access the data they need in real-time, RAG chatbot solves major pain points, unlocks productivity gains, and enhances product quality in manufacturing.

Understanding generative AI and RAG

Generative AI is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. Generative AI is built on large language models (LLMs) trained on a vast volume of data and uses billions of parameters to generate original output for solutions like question answering, chat bots, etc.

RAG is the process of optimizing large language models with external knowledge bases. This “augments” the LLMs capabilities, allowing it to provide responses tailored to specific domains. In manufacturing, RAG links the LLMs to vast repositories of assembly drawings, instructions, and specifications. When assemblers ask questions, the LLMs can reference this manufacturing knowledge base to generate useful, on-point responses. This gives assemblers a digital assistant that truly understands their domain.

Additionally, RAG enhances LLMs without expensive retraining. The LLMs simply leverage the external data to stay accurate and relevant. This makes RAG a cost-effective way to optimize LLMs for assemblies.

Value of generative AI RAG chatbot on AWS 

Contract manufacturers are not required to construct their own generative AI infrastructure. Instead, they can leverage their data with the utmost security and privacy while retaining complete control over it. Amazon provides secure tools for building RAG chatbots that accelerate contract manufacturing assemblies. Amazon vector database offerings like Amazon Open Search, Amazon Aurora PostgreSQL, Amazon Neptune, etc. enable chatbots to tap into vast knowledge bases of assembly drawings, specifications, and instructions stored as vectors. Amazon Bedrock simplifies building and scaling of generative AI applications with foundational models (FMs). Knowledge Bases for Amazon Bedrock automate the end-to-end RAG workflow, including ingestion, retrieval, prompt augmentation, and citations, eliminating the need for you to write custom code to integrate data sources and manage queries.

Learn how Infosys, an AWS Partner, built a generative AI solution on Amazon Bedrock to help aircraft maintenance, repair, and overhaul (MRO). This helps original equipment manufacturers (OEMs) reduce the expenditure on document search, analysis, interpretation, and management, which currently amounts to approximately $5 billion USD.

Accelerating time to assemble

RAG chatbot allows assemblers to bypass the traditionally time-consuming process of digging through manuals and assembly drawings. Instead, they can get instant answers to their questions in plain language. RAG chatbot leverages knowledge bases to surface the precise assembly drawings, specifications, and instructions that assemblers need in real-time. By simply querying the RAG chatbot, assemblers can quickly locate the relevant data without having to interpret complex drawings themselves. This allows them to make rapid decisions and proceed with assembly tasks unimpeded. RAG chatbot removes the data access bottlenecks that have long slowed down assemblers, unlocking dramatic productivity gains. Learn how Siemens’ Mendix allows their customers to apply generative AI to drive productivity within their workforce.

Preventing errors and rework

Misinterpreting complex assembly drawings often lead assemblers down the wrong path, resulting in errors that require expensive rework. RAG chatbot helps assemblers avoid these pitfalls by identifying relevant data and potential issues before problems arise. The RAG chatbot guides assemblers step-by-step through assembly processes. By highlighting ambiguities in real-time, it enables assemblers to catch inconsistencies immediately rather than after the fact. RAG chatbot proactively serves up the precise assembly drawings, specifications, and instructions needed, reducing guesswork. With RAG chatbot, assemblers no longer waste hours searching across fragmented data sources. The right information comes to them. This prevents errors by arming assemblers with accurate and up-to-date insights. By getting ahead of potential issues before they become costly problems, RAG chatbot enhances quality and reduces the need for wasteful rework. Assemblers gain confidence by having a digital assistant at their fingertips, ensuring they follow the optimal workflow.


RAG chatbot represents a pivotal shift for contract manufacturers. By giving assemblers an on-demand digital assistant, it solves two of the biggest assembly challenges – slow access to data and preventable errors. This results in faster assembly times, less rework, and higher quality. By embracing RAG chatbot, contract manufacturers gain a real competitive edge. They can deliver higher value to customers through faster and more flexible assembly. RAG chatbot helps position contract manufacturers for success amid ever-increasing complexity and customer demands. Contract manufacturers are invited to leverage AWS generative AI tools to accelerate assembly time, prevent errors, and unlock productivity gains.

Lesley Ajanoh

Lesley Ajanoh

Lesley Ajanoh is a Data & AI Partner Development Specialist for North America at AWS. He collaborates closely with AWS partners to develop their Data & AI strategy to drive customer adoption and revenue of AWS Data & AI Services. Empowering AWS partners to help customers across industries achieve growth, productivity, and innovation in AWS.

Milan Thanawala

Milan Thanawala

Milan Thanawala is the Global Head for Database, Partner Center of Excellence at AWS. With over 25 years of experience in product management, strategy, and business development, he collaborates closely with AWS Partners to boost adoption and revenue for Amazon Database Services. Prior to AWS, Milan was Sr. Director at Oracle leading Partner Strategy & Evangelism for various cloud services. He holds an MS in Electrical Engineering from Virginia Tech and an MBA from Santa Clara University.