We provide AI R&D as a Service (RDaaS), combining expertise in Artificial Intelligence with a proven R&D process. Our mission is to develop custom AI solutions to create new competitive advantages and IP for our Clients. Through our AI and R&D expertise, we provide faster time to market and predictable AI developments.
WHY AI R&D AS A SERVICE
Artificial Intelligence is becoming fundamental to all industries at exponential speed.
However, in contrast with other technologies, developing a customized AI system requires a very knowledgeable AI R&D team, which is costly and rarely justified as a permanent company structure. DEEP AI Lab provides an AI capability as a service, developing and realizing AI-based solutions tailored to the client’s operational challenges and priorities.
Specialized in developing AI solutions, prototypes and proof-of-concept, we support our Clients and partners with shorter development times and more cost-efficient product development.
This is AI R&D as a service.
HOW WE WORK
By deep-diving into your specific industry or domain, we help solve YOUR technical challenges.
Leveraging specialized know-how and data we develop the right AI tools able to provide a new competitive advantage or improve your operations. We adopt a Design Thinking approach to immerse into your unique challenges while bringing our Artificial Intelligence and R&D expertise.
For instance, to help with challenges faced by an Interventional Cardiologist, we had first to become sufficiently acquainted with the medical processes in the cath lab before effectively layering up our expertise in Computer vision and AI. However, the same techniques we developed to track a guidewire in a fluoroscopy image could also be a good starting point to add AI to a commercial drone to identify and skip dangerous overhead power lines.
AI and Design thinking can be applied to any problem in any industry, as long as a sound, predictable AI R&D process is applied.
Through our “R&D as a Service” projects we deliver cutomized AI solutions to our Clients in 8-12 weeks on average.
Idea
After a free discovery call with a client, we usually capture enough understanding to develop a detailed roadmap with a timeline and clear deliverables. Then we deep-dive into the Client domain and understand the specific challenge to solve or the core competitive advantage to achieve. We use the Design Thinking approach to ensure the result is human-centric and that we solve the right problem.
Design
We then create a solution architecture design, identifying the data pipeline and best-fitting Deep Learning architectures. Data are rarely ready to be used: we often assist our Clients in anonymizing sensitive user data, standardising and cleaning them for exploratory data analysis and labelling.
Development
Our Deep Learning R&D cycle adopts Agile and Design Thinking methodologies to embed the Client in the development while we track, control and iterate. We then launch training and hyperparameter tuning cycles, optimizing the model.
Deployment
We develop the package, service or API for production based on the specific application. Then, we set up the tools to monitor performance and key metrics and identify anomalies. Finally, we provide the solution with full documentation and knowledge transfer to the Client’s team.
THE BLOG
Judge the AI Regulation: the best four key questions.
The actual implementation of new EU regulations for AI should stick to the common sense we use for any technology and specifically should be checked against some key questions: I call this list a “common-sense checklist”. We can use it to score a given regulation, but it is also a valuable framework for discussing the […]
Boost your best AI ideas with a Design Thinking proven approach
Brian Cheski, co-founder and CEO of Airbnb, defined Design Thinking best as “…starting with the perfect experience and working backwards”. Design Thinking is a powerful approach to designing anything, from services to physical furniture. So while the actual steps for developing an online accommodation marketplace like Airbnb might differ from designing the iconic Herman Miller’s Aeron […]
Left atrium segmentation with neural network
Left Atrium segmentation is a crucial step in cardiac disease diagnosis. Changes in the atrial volume are associated with cardiac diseases such as atrial fibrillation or mitral valve stenosis. Often the segmentation of related LGE-MRI images is carried out manually, a time-consuming and error-prone process. Therefore, we developed a neural network to automatically segment the […]