Enterprise Grade Lean AI platform

Información del proyecto

BrainMatter

Identificador del acuerdo de subvención: 867316

DOI

10.3030/867316

Proyecto cerrado

Fecha de la firma de la CE 11 Mayo 2019

Fecha de inicio 1 Mayo 2019

Fecha de finalización 31 Agosto 2019

Financiado con arreglo a

• INDUSTRIAL LEADERSHIP - Innovation In SMEs

Coste total

€ 71 429,00

Aportación de la UE

€ 50 000,00

Coordinado por

BRAINCREATORS BV
Netherlands

Periodic Reporting for period 1 - BrainMatter (Enterprise Grade Lean AI platform)

Período documentado: 2019-05-01 hasta 2019-08-31

Resumen del contexto y de los objetivos generales del proyecto

AI models are trained via supervised learning using huge amounts of categorized data. Preparing the data is very time-consuming, because currently the data needs to be manually labelled. As a result, productivity is low, and costs are high. Secondly, there is a general lack of large high-quality data for the models to be accurately trained. Thirdly, AI platforms are narrowly focused on just a few data types.
Overall aim of BM is to develop an intelligent and highly customizable platform, that facilitates the full AI lifecycle from the creation of high-quality data sets independent from the data source to the training and deployment of AI models.
The importance for the society of the BM project lies in paving the way to industry-wide AI adoption which is a huge market estimated to create $3.7T value by 2027.

Trabajo realizado desde el comienzo del proyecto hasta el final del período abarcado por el informe y los principales resultados hasta la fecha

Technical feasibility: we defined a technical roadmap, including work plan for the coming two years with technical specifications, and further a path to reach TRL9. We have analysed the technical risks and defined a contingency plan. Our planning also includes task-by-task planning of budget and time.
Commercial feasibility: we carried out a market analysis of the Big Data and AI for enterprise applications markets (Western EU and global scope): software market to reach $46B value by 2027 with 16% CAGR growth. We narrowed down our target verticals to manufacturing and defined the market drivers, barriers, as well as conducted a SWOT analysis; we also benchmarked against competitors to re-define USPs. We identified stakeholders, standards and regulations. We concluded an FTO and defined an IP protection strategy. We updated the business model canvas with findings of the study, including revenue model and pricing.
Financial viability: according to the findings of the technical and commercial studies we updated our financial projections with the re-defined revenue and pricing model and deemed BM to be financially viable.

Avances que van más allá del estado de la técnica e impacto potencial esperado (incluida la repercusión socioeconómica y las implicaciones sociales más amplias del proyecto hasta la fecha)

We have signed a collaboration project, BrainPower, with IBM that will allow us to test our solution in their machines. The revenue from the collaboration with help us to finance further development activities.
The wider impact of BM is allowing the adoption of AI industry- wide and drastically speeding up automating of error-prone, time-consuming and boring data labelling tasks which will allow data scientists to centre themselves to tasks of higher-value creation and therefore contribute to unleash the potential of $3.7T AI value.

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