Summary
The SmartMap project will achieve what has not been possible previously – to provide a fully AI automated, data-ownership enabled, indoor-map generation and wayfinding software. MazeMap AS are global first-movers and we have developed a unique AI/Machine Learning solution for 1) automating map generation at low-cost and 2) allowing customisation, integration and ownership of the data for customers. MazeMap has a proven business case as through a Phase 1 Feasibility Study (Project ID: 745040) with NTNU and St. Olav’s University Hospital. At NTNU, MazeMap helped 44,000 students and employees find their way more than 65,000 times/week during 2018 and the university estimated a yearly savings of €540,000 using SmartMap services. At St. Olav’s University Hospital SmartMap helped reduce the missed appointments by over 30 % through and integration with the hospitals appointment system. The reduction of missed appointments contributed to €1.5 million in yearly savings as well as shorter patient queues . Since the feasibility study MazeMap have reached over 1 million signed-up beta testers from across 26 countries globally. The SmartMap Phase 2 project aims to mature and commercialise a fully AI-automated SmartMap system. The funding will allow us to improve our automation rates enabling zero-cost productions going forward and develop more FMS integrations while piloting the finalized platform. Our solution belongs on the indoor mapping market segment focusing on universities, hospitals and large office buildings with an estimated value of €2.6 billion and growing at 40% yearly. The potential for growth is excellent as only 0.001% of buildings have been mapped globally. Based on our proven business case and current growth we expect to accumulate €70 million in revenues and €30 million in profits in the first five years of commercialisation and creating over 90 new FTE jobs.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/872288 |
| Start date: | 01-10-2019 |
| End date: | 30-09-2021 |
| Total budget - Public funding: | 2 959 375,00 Euro - 2 071 562,00 Euro |
Cordis data
Original description
The SmartMap project will achieve what has not been possible previously – to provide a fully AI automated, data-ownership enabled, indoor-map generation and wayfinding software. MazeMap AS are global first-movers and we have developed a unique AI/Machine Learning solution for 1) automating map generation at low-cost and 2) allowing customisation, integration and ownership of the data for customers. MazeMap has a proven business case as through a Phase 1 Feasibility Study (Project ID: 745040) with NTNU and St. Olav’s University Hospital. At NTNU, MazeMap helped 44,000 students and employees find their way more than 65,000 times/week during 2018 and the university estimated a yearly savings of €540,000 using SmartMap services. At St. Olav’s University Hospital SmartMap helped reduce the missed appointments by over 30 % through and integration with the hospitals appointment system. The reduction of missed appointments contributed to €1.5 million in yearly savings as well as shorter patient queues . Since the feasibility study MazeMap have reached over 1 million signed-up beta testers from across 26 countries globally. The SmartMap Phase 2 project aims to mature and commercialise a fully AI-automated SmartMap system. The funding will allow us to improve our automation rates enabling zero-cost productions going forward and develop more FMS integrations while piloting the finalized platform. Our solution belongs on the indoor mapping market segment focusing on universities, hospitals and large office buildings with an estimated value of €2.6 billion and growing at 40% yearly. The potential for growth is excellent as only 0.001% of buildings have been mapped globally. Based on our proven business case and current growth we expect to accumulate €70 million in revenues and €30 million in profits in the first five years of commercialisation and creating over 90 new FTE jobs.Status
CLOSEDCall topic
EIC-SMEInst-2018-2020Update Date
27-10-2022
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Geographical location(s)
Structured mapping
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