SaaS platform applying Artificial Intelligence and collaborative learning to the automation of Machine Vision-based manufacturing processes

Project Information

UniMind

Grant agreement ID: 867535

DOI

10.3030/867535

Project closed

EC signature date 6 May 2019

Start date 1 March 2019

End date 31 August 2019

Funded under

• INDUSTRIAL LEADERSHIP - Innovation In SMEs

Total cost

€ 71 429,00

EU contribution

€ 50 000,00

Coordinated by

PIPLUS SERVICIOS PROFESIONALES SL
Spain

Periodic Reporting for period 1 - UniMind (SaaS platform applying Artificial Intelligence and collaborative learning to the automation of Machine Vision-based manufacturing processes)

Reporting period: 2019-03-01 to 2019-08-31

Summary of the context and overall objectives of the project

About 99% of the companies in Europe are Small and Medium Enterprises (SMEs) that are facing the exact same challenges that any big company. Particularly in manufacturing, European companies are encountering digitalization and automation complexities that could have long-term impact in their competitiveness in global markets. Today, experts are saying manufacturers that do not have Artificial Intelligence technologies will not be able to compete over time.

BitMetrics believes Artificial Intelligence may be the key enabler to affordable automation and is currently developing UniMind, a SaaS platform that democratizes access to intelligent automation of industrial processes. UniMind is based on three pillars: machine vision (for context recognition from image sources), deep learning (to understand the environment) and robot learning (to make the robot flexible to adapt to variations in the setting).
UniMind enables companies to rely on the platform’s inherent ability to recognize new or changed variables in the context and adjust the robot’s action without human intervention, meaning that companies do not need to have dedicated (and specialized) teams to oversee these variations and modify the system to perform accordingly.

The UniMind Feasibility Study – Phase 1 conducted under the agreement number 867535 aimed to assess the overall viability of BitMetrics’ business model in a competitive environment, the initial business model selected, the technical possibilities that needed further exploration as well as the potential of innovation. As a general conclusion, BitMetrics gained a deeper understanding of the market and was able to adapt its business model gearing towards a new product and market strategy that will have better chances for success.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The 6-months period covered by the Feasibility Study went from March 1st to August 31st 2019. The key tasks BitMetrics committed to execute included:
- a complete market study to examine in further depth competition and to estimate/validate the potential market size in Spain, Germany and Italy. This analysis let us recognize and assess competition, their strategies and business models. It also helped us to identify named potential business partners in the three countries.
- a regulatory study. We understood the need to considered not only data privacy regulation but also Regulation (EU) 2019/881 that consists of “a framework for the establishment of European cybersecurity certification schemes for the purpose of ensuring an adequate level of cybersecurity for ICT products, ICT services and ICT processes in the Union that is also an opportunity for BitMetrics to outstand from competitors by acquiring the necessary certificates to become a trusted company.
- an IPR protection plan, that resulted in an external assessment made by a Spanish intellectual property firm that recommended us to apply for an invention patent
- Our Neural Network model evaluation after which we consider BitMetrics is in a position to use our current proprietary Deep Learning models and methodology more reliably and competitively in a production setting.
- a 5-year business plan, with a detailed
- We also had the chance to receive business coaching from a seasoned business executive that challenged our hypothesis by asking the right questions in order to accelerate our start-up’s development.

In terms of dissemination, we spread the word about the project and its outcomes in any presentation made by BitMetrics (from recruiting pitches to commercial meetings, as well as through our webpage http://bitmetrics.es/unimind). We also took control of our profile at Crunchbase and added additional information (https://www.crunchbase.com/organization/bitmetrics)
We noticed the positive effect that communicating that our project has been funded by the European Commission has on any potential partnerships: immediately we gain respect and potential in front of our counterparts.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

BitMetrics is building solutions for industrial automation based on robot learning by means of DL (Deep Learning) and RL (Reinforcement Learning). Such solutions are based on integral robot learning, with no human knowledge or expertise involved in it. Robots learn tasks as they operate: the more they operate the better for learning purposes. They can do so in any context and, as such, provide fully flexible, integral solutions that would allow the automation of some processes for the first time, while improving existing ones.

State-of-the-art evaluation allowed us to confirm that big improvements in robot learning are being reported in the academic field, yet no industrial solutions are built around it as of today.

Automation is an enabler for companies to be able to compete in a glocal context. Helping SMEs access affordable automation is essential to unlock efficiencies.

Another key aspect to consider, is that the automation of these types of tasks contribute to free up workers so they can take on more challenging and meaningful work. This approach helps lead to better organizational climate affecting how the end-client company is “perceived” by employees and their level of satisfaction which is echoed to other relevant stakeholders in society.

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