Proximai’s mission is to advance the aerospace industry through emerging AI technology. We see that demand is high in companies for AI experts, but recruiting them (along with the need for data and compute power) is costly to arrange for the occasional project.
By offering consulting services specialized on AI, we can offer a cost-efficient approach for your selective projects.
A feature of our Global AI Network (GAIN) process includes individuals that retain the necessary credentials or experience in either machine learning or deep learning systems. For example, the SME may have an industry-certification from NVIDIA or Microsoft. Alternatively, they may retain or are a candidate with necessary academic credentials in either data science or software engineering. As a result, your project benefits by having a check-and-balance as to the application of “best-practices.”
Machine learning, deep learning and AI are highly dependent on large, diverse, and structured datasets. If no dataset can be provided for an application, strategies exist to assist in data collection, processing, and tagging to ensure a well-trained generalized model in certain circumstances. The success of the models are a function of scrubbed, relevant data. As a result, your organization can benefit by lowering overall risk or facilitating access to data in usable, secure containers at Microsoft Azure.
One feature of our service is investing in best-of-class solutions. For example, NVIDIA DGX servers equipped with the latest hardware designed for deep-learning powers model training. Likewise, we are a certified partner with Microsoft Azure to provide an enterprise-ready cloud solution for hosting and inference of our neural network models. As a result, this benefits an organization by reducing processing time or reducing risk with a standards-based approach to ensure continuity.
We support Public and Private partnerships at all levels ranging from the Aerospace and Computer Science student intern to the Ph.D. fellow to the retired Aerospace engineer. For example, from within the field of data science and AI, a lot of time is spent managing and preprocessing datasets. We believe selected, managed student interns can cost-effectively accomplish these tasks as needed for your project. In contrast, the wisdom of the retired aerospace engineer often can provide realistic input not found elsewhere. As a result, these combined resources increase your competitive, differentiated advantage.
To assist your team’s needs, we offer these remote resources under AI-as-a-Service (AIAAS). It is evident that the Aerospace Industry could benefit from integrating key AI components into existing software, or considering it when approaching unsolved problems.
Contact us today to see what AI-as-a-Service (AIAAS) can do for your organization.
A universal issue for companies that develop technology today is the process of obtaining data. This becomes an even greater issue when wanting to integrate machine learning (AI). We want to make it easier for companies to take the full advantage of AI today.
It’s no secret that with more data you are simply able to do more. In order to be successful with AI, it requires large datasets, which sometimes are unavailable due to cost and time restrictions.
When a rocket launch is delayed, it can negatively affect both the launch provider and payload client. Our proposed system works by training an AI model on a large variety of data:
By complimenting existing flight safety procedures, this model will predict the best days to launch out of a predefined (user-set) window. Time, money, and client confidence are all saved as a result.
To date, there are no complete turnkey systems that allow an engineer to simulate, visualize, and predict combustion flow in jet and rocket engines. We propose using machine learning rather than traditional models (like the Navier-Stokes equations) for fluid flow and chemical physics. While there does exist traditional CFD software ran on CPUs with “Combustion add-on packages”, they have their limitations.
Our solution will apply a Deep Neural Network trained to predict combustion effects. We envision that our system would assist engineers in the design process by cutting simulation time and reducing costs for HPC clusters. Once completed, the system will have the following features & benefits:
Below are discussions on the general company structure, the aerospace field, and job opportunities with Proximai.
Proximai is a new start-up business unit of a California General C Corporation that was formed in 1997.
Our main office is at 320 Alisal Rd #101, Solvang, CA 93463. We also have a workspace at Cal Poly CIE, San Luis Obispo.
Proximai was founded with a passion for space exploration. One key subspace is propulsion, which has a large impact on cost, effective range, and availability of rocket launches.
We are designing our models and tools specifically to empower engineers. We hope with these new methods of analysis they will be able to solve problems in ways that haven't been considered before.
We need data scientists, software engineers, but most importantly: people who understand how to bring together novel ideas. All members will become a part of our international community: GAIN.
Please send us an email, or call and talk about what interests you.