Aerospace companies are at the forefront of technological innovation, constantly seeking ways to improve efficiency, streamline operations, and maintain their competitive edge. With the rapid advancements in artificial intelligence, many are beginning to explore how Generative AI (GenAI) can be leveraged to solve some of the most persistent challenges they face—including core operational inefficiencies that impact everything from day-to-day decision-making to long-term strategic initiatives.
GenAI has the potential to revolutionize the aerospace industry by offering solutions to problems that have long plagued the sector. As the aerospace workforce continues to evolve and the volume of data generated by complex systems and technical processes increases, there’s a growing need for tools that can help companies manage, analyze, and utilize this information more effectively.
However, the adoption of generative AI in aerospace comes with unique challenges. The sensitive nature of aerospace data, along with the life-or-death implications of many engineering decisions, means that any AI solution must not only be powerful, but also reliable, secure, and highly customizable to the needs of the industry. As companies evaluate how to integrate GenAI into their workflows, they must carefully consider how it can meet these stringent demands while addressing their key pain points.
Three of the most significant challenges currently facing the aerospace industry—knowledge loss due to employee turnover or information silos, fragmented information across disparate systems, and the difficulty of navigating lengthy technical documentation—are ripe for AI-driven solutions. Addressing these issues with GenAI can provide aerospace companies with a transformative tool that enhances operational efficiency, improves safety, and supports better decision-making.
Knowledge loss can occur because information is often held either by a single individual or team. For example, an engineer may encounter an unusual problem and work to find a solution. Once they do so, that knowledge is likely to remain solely with them. Such silos only compound when tenured experienced employees leave and take years—sometimes decades—of institutional knowledge with them, creating critical gaps in expertise and operational continuity.
Similarly, fragmented information across systems poses a major challenge. Aerospace companies typically rely on a variety of complex, and often outdated, systems that don’t easily integrate or communicate. These systems can be cumbersome to use, lacking intuitive interfaces and effective search functionalities, further complicating access to crucial information.
Even when the right data is identified, navigating extensive technical documentation can be another obstacle. Manuals and technical reports often span hundreds of pages, making it difficult to locate specific details quickly. Engineers may know which document contains the information they need, but pinpointing that data can feel like searching for a needle in a haystack.
Yurts Enterprise AI addresses each of these challenges with a comprehensive AI-driven solution:
While these capabilities are transformative, they are just the foundation for success in the aerospace sector. Aerospace companies also face unique concerns related to data privacy and security. Much of their data is sensitive and compartmentalized, and must be kept secure. Yurts addresses these needs by offering options such as hosting data within the company’s own cloud or deploying large language models (LLMs) on-premise, ensuring that sensitive information never leaves the organization’s control. Built-in permissions and guardrails further ensure that data remains appropriately secure and accessible only to authorized users.
Additionally, accuracy and verification are non-negotiable in aerospace, where engineers' decisions directly impact safety and operations. Trusting AI solutions blindly is not an option. Yurts is not only the most accurate solution on the market, but it also offers full transparency, providing inline citations, previews of the source text, and links back to original documents for every AI-generated response. This gives engineers the ability to not just trust—but verify—all of their answers.
Some aerospace companies are exploring the development of their own GenAI solutions using open-source tools or existing software. Many have built prototypes that they’ve begun to demonstrate both internally and externally. The key decision they now face is how much responsibility they want to take on for building and maintaining these systems at the enterprise-wide level.
While setting up a basic GenAI-powered chat experience using an LLM and retrieval-augmented generation (RAG) might be straightforward, creating and maintaining a solution that is highly accurate, trustworthy, secure, fully integrated across systems (Sharepoint, Teams, etc..), efficient, and flexible is far more complex and resource intensive. We haven’t even talked about the compute costs, which Yurts helps companies control with its built in MLOps platform and use of smaller models.
The Yurts platform enables teams to easily deploy and manage GenAI technology while embedding it into existing enterprise workflows and applications. This turnkey solution minimizes the need for development teams to get involved in the nuts and bolts, allowing companies to quickly bring AI-powered capabilities to life. For those who want to customize or extend the platform, Yurts also offers a wide range of APIs and configurable options.
Thinking about introducing GenAI at your aerospace company? Get in touch!
.png)
