100 ExperimentsIdea to MVP in 9 - 18 months
How 100 Experiments Works
Matching the industry to the top AI minds in Singapore to solve their AI problem statements.
100E funds the assembled academics and researchers in the IHLs and RIs up to $250,000 to work on the project sponsor’s problem statement.
The project sponsor is expected match funding 1:1 in-kind (engineering manpower) and in-cash.
The AI Singapore ecosystem currently consists of: National University of Singapore (NUS), Nanyang Technological University (NTU), Singapore Management University (SMU), Singapore University of Technology and Design (SUTD), Singapore University of Social Sciences (SUSS), Singapore Institute of Technology (SIT), and Agency for Science, Technology and Research (A*STAR).
Come to us with a potential problem statement where AI could be used to solve it effectively. We will help you scope, distill and refine it, and then post to the AI ecosystem of researchers.
AI Singapore will try to find and match a suitable researcher to your project.
Qualified researchers will submit their proposal for evaluation after they have met you and worked with your team to better understand the problem.
AI Singapore together with your team will qualify and select the most appropriate proposal for you.
We will award up to $250,000 to the researchers to work on your problem statement in a 1:1 matching grant.
Any Singapore registered company or government agency.
NUS, NTU, SMU, SUTD, SUSS, SIT and A*STAR
Up to $250,000 to the project performers.
AI Singapore will provide 1:1 funding to support the project.
Project sponsors are to match 1:1 with 30% cash and 70% in-kind.
In-kind refers to companies’ engineering resources allocated for co-development.
MVPs are typically 9-18 months.
All R&D to be done in Singapore.
Experienced software engineers from the AI Singapore team and AI Apprentices from the AI Apprentice Programme will be paired with researchers and your team.
AI Singapore’s own experimental AI infrastructure, NSCC production-grade systems and public cloud.
MVP must deploy into production.
It should delight your users and customers.
Director, AI Industry Innovation
Head, AI Applications
Head, AI Platforms
Associate Director and Head, AI Engineering