NDRC unveils five new start-ups for Dublin-based accelerator
Health, machine learning, aquaculture and autotech companies included in latest cohort
Representatives from the newest round of start-ups backed by State-backed NDRC: (l-r) Ken Morgan of Spire; Peter Clifford, MEG; Conor Wilson, Sproose; Edel Churchill, MEG; Kate Dempsey, Aqua Licence; Kerrill Thornhill, MEG; and Pat McKenna, Sproose. Photograph: Shane O’Neill
The State-backed NDRC has unveiled the newest round of start-ups backed by its Dublin-based accelerator programme, with five companies getting €135,000 each.
The digital start-ups, which include health, machine learning, aquaculture and autotech companies, will now embark on a six-month acceleration programme based at NDRC’s Digital Exchange offices.
The €135,00 investment includes €100,000 in cash, with the remainder in supports.
Almost 300 businesses have been supported by NDRC, with 17 ventures receiving follow-on funding of more than €500,000 last year. Its portfolio includes Nuritas, SilverCloud Health, Newswhip, and Boxever.
“Bringing a start-up from an idea right through to being ready for investment is a challenging process,” said chief executive of NDRC Ben Hurley.
“With our proven expertise and focus, NDRC is here to help with that progression. A good mix of entrepreneurs in this spring programme will lend itself to an interesting dynamic, with these businesses already showing some progress with their propositions.
“Some are already revenue generating, with enhanced customer discovery already well underway among the founders but the challenge of securing seed investment remains due to changes in the funding ecosystem.”
The five new companies include healthcare focused MEG, which creates mobile first, paperless tools to help improve the quality of healthcare; Sproose, which offers companies a suite of services to businesses such as laundry, bike repair, couriers, dentistry; vehicle incident management company Spire; Aqualicense , which improves wait times for organisations seeking aquaculture licenses; and Machine Learning Programs, which predicts the likelihood of people making claims, using machine learning and statistical models.