Developing an industry-transforming AI solution is not just a competitive advantage, it’s a game-changer. However, achieving this goal requires a significant investment in talent and resources, as well as a real business transformation. The challenge lies in the vast skills gap between the demand for specialized AI roles and the limited pool of available researchers. True AI innovation in business comes from a unique combination of fundamental and applied research, bridging the gap between pure science and solution-focused work.
An AI R&D (research and development) lab is specifically designed to tackle this challenge. It brings together the best of academic research and industry-centered goals, forming a multidisciplinary team with deep AI expertise. This team is dedicated to achieving a business partner’s specific AI objectives, says Max Frolov, CEO and co-founder of DataRoot Labs.
“AI R&D merges the real-world requirements of the technology sector with a focus on pioneering new methods and practical application technology concerns. It offers experimental solutions to unsolved challenges,” explains Frolov. “Our AI R&D data scientists are committed to tackling the big challenges that are reshaping industries, pushing boundaries, and ultimately creating valuable intellectual property that gives businesses a competitive edge and increases their valuation.”
While many companies in Silicon Valley have their own internal or offshore AI R&D labs, Frolov emphasizes the advantages of an external AI R&D facility. It can expand a company’s technological frontiers, regardless of its size, and future-proof the business in a fraction of the time and cost required to build an internal team.
So, how do these external AI R&D centers work? They consist of interdisciplinary teams of machine learning researchers, engineers, MLOps, and NLP professionals. Through collaboration, agility, and affordability, these centers co-build next-generation products and services, often leading to unexpected and groundbreaking outcomes.
Unlike traditional consulting models, AI R&D labs provide continuous support and expertise to crack unresolved tech challenges and deliver tangible results. The teams analyze technical tasks, research landscapes, and market demands to develop minimum viable products and continuously improve them.
One of the key advantages of an external AI R&D facility is its ability to address the resource challenges faced by the industry. By tapping into a vast and diverse engineering pool outside of traditional boundaries, companies can access a balanced range of seniority levels and benefit from stable talent. In a lab environment, engineers have the opportunity to work on thought-provoking projects, expand their expertise, and advance their careers.
Moreover, going offshore for AI R&D offers cost advantages. Countries like Ukraine, with their strong technical talent pool and lower development costs, are becoming attractive options for companies. These countries also invest in their talent pool through partnerships with universities and research organizations, ensuring a continuous supply of skilled data scientists.
For example, DataRoot Labs collaborates with top local universities, such as Kyiv Polytechnic Institute, to offer tech education and establish AI master’s programs. They also run their own free online school, DataRoot University, which supports students in applying their technical knowledge to real-world AI startup projects.
By working with offshore AI R&D centers, companies not only benefit from cost savings but also contribute to the growth of AI ecosystems in these countries. It’s an opportunity to make a difference and support nations facing challenging circumstances.
