What challenges did Aerospace Industry Customers face before AI implementation?

Aerospace companies struggle with the knowledge transfer gap as experienced subject matter experts retire, risking loss of critical operational knowledge. Technicians require fast and authoritative access to large and complex libraries of technical documents to perform their tasks effectively. There is a need to maintain data privacy, minimize hallucinations, and ensure answer accuracy within generative AI applications in a safety-critical domain.

What was the impact of the AI solution?

Technician training and knowledge accessibility were accelerated, reducing dependency on retiring SMEs and improving operational decision-making. The RAG chatbot enhanced workforce productivity by providing fast, accurate, and traceable expert guidance at scale. Data privacy and model accuracy were maintained through isolated knowledge bases and controlled document access. This scalable generative AI solution has potential applicability across multiple aerospace lifecycle functions beyond maintenance.

Source

Scaled productionEvidence: Low40/100

GenAI for Aerospace: Empowering Workforce with Expert Knowledge Using Amazon Q and Amazon Bedrock

Use case typeAI platformUpdated Jun 13, 2026

Aerospace industry customers face workforce challenges due to SME retirements and the need to transfer complex technical knowledge quickly and accurately. AWS implemented a Generative AI-based Retrieval Augmented Generation (RAG) chatbot solution to provide technicians with expert-level guidance derived from FAA technical documentation. The solution uses Amazon Q for no-code chatbot deployment and Amazon Bedrock Knowledge Bases for flexible and accurate knowledge management with proprietary data. The RAG architecture ensures trusted, secure, and up-to-date answers with direct attribution to source documents, reducing reliance on SME availability and accelerating technician training.

Organization: Aerospace Industry Customers
Industry: Manufacturing
Location: United States
Published: September 2024

Reported outcomes

Strategic outcomes

Speed & agilityAccelerated technician training and knowledge accessEmployee experienceImproved workforce productivityRisk & complianceMaintained data privacy and accuracyNew product / capabilityDeployed scalable expert guidance chatbot

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Aerospace Industry Customers
Provider: AWS
Maturity: Scaled Production
Linked source: AWS Machine Learning Blog

The RAG chatbot enhanced workforce productivity by providing fast, accurate, and traceable expert guidance at scale

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1Generative AI
2RAG Chatbot
3Knowledge Management

Aerospace companies struggle with the knowledge transfer gap as experienced subject matter experts retire, risking loss of critical operational knowledge.
Technicians require fast and authoritative access to large and complex libraries of technical documents to perform their tasks effectively.
There is a need to maintain data privacy, minimize hallucinations, and ensure answer accuracy within generative AI applications in a safety-critical domain.

AWS provided a cloud generative AI solution combining Amazon Q and Amazon Bedrock to deploy RAG-based chatbots.
Amazon Q enables quick setup of no-code, scalable chatbots for technicians to access FAA and aerospace domain knowledge.
Amazon Bedrock Knowledge Bases offers advanced API-level control over knowledge base components, embeddings, and LLM model selection to fine-tune accuracy and security.
The solution securely integrates with Amazon S3 for document storage and uses OpenSearch Serverless for vector search of document chunks, ensuring efficient, reliable retrieval and generation of answers.

Technologies

Amazon Q Amazon Bedrock Amazon S3 Amazon OpenSearch Serverless

Technician training and knowledge accessibility were accelerated, reducing dependency on retiring SMEs and improving operational decision-making.
The RAG chatbot enhanced workforce productivity by providing fast, accurate, and traceable expert guidance at scale.
Data privacy and model accuracy were maintained through isolated knowledge bases and controlled document access.
This scalable generative AI solution has potential applicability across multiple aerospace lifecycle functions beyond maintenance.

Architecture

The architecture employs a retrieval augmented generation pattern using Amazon Q and Amazon Bedrock, connected to an S3-hosted document repository of FAA technical manuals. Amazon OpenSearch Serverless enables vector-based semantic search for relevant document chunks, which are combined with queries and sent to large language models in Amazon Bedrock for response generation. Access and data privacy controls are integrated via AWS IAM and fine tuning of embeddings and chunking strategies are configurable.

Sources & evidence1

Evidence: Low40/100Evidence strength