AlgoLib is a new start up business created by a local Liverpool City Region PhD Student that collects and analyses big data and smart algorithms to offer businesses analytic insights, economic scenario generators, intelligence and innovative solutions based on strategic and risk management recommendations; all via speed utilised databases and centralised algorithm libraries.
Approaching LCR 4.0
AlgoLib wanted to extend their services by also offering their customers the opportunity to have their data collected and displayed onto a cloud platform in real time which has not yet been done within the Industry. This will allow for the sharing of data to allow companies to see a larger range of data, helping to make better informed business decisions. The sharing of data is to include algorithms, business solutions and published projects as AlgoLib hope to work alongside businesses such as insurance companies, developers, finance institutions and Universities.
AlgoLib is the first SME to enter the LCR 4.0 project based on cloud applications. The LCR 4.0 team worked with AlgoLib to ensure that their intellectual assets were properly identified, protected and employed. Each pillar of Industry 4.0 presents distinct legal, risk and commercial concerns. The VEC (Virtual Engineering Centre) worked with AlgoLib to assess their methods and develop appropriate provisions to ensure they were recognised as a commercial asset.
The team also reviewed competing technologies and recent investments in data analytics within start-ups and provided an Industry 4.0 based critique of contracts, insurance, business continuity and development opportunities.
The LCR 4.0 project has resulted in AlgoLib pivoting its operational approach to ensure the company’s IP and assets can be employed effectively and competitively in a fast moving, client centric market place.
Data analytics is a rapidly growing field that presents new challenges. The VEC drew upon their collective experience of IP protection and licensing, cloud computing and software development to help AlgoLib rationalise their development roadmap and prepare the company for the rigours of investor due diligence in the future.
Working to the Future
It would take AlgoLib 22 seconds to perform 1 calculation and required around 1,000 calculations to collect the data. The VEC helped AlgoLib apply their technology to high performance computing platforms, resulting in the process time being accelerated 300 fold. The innovation improves both the turnaround time and capacity of the company to undertake work.
Following interactions with the LCR 4.0 project, AlgoLib has identified opportunities to provide big data products and services to the manufacturing sector and realised the potential to improve productivity by undertaking larger more complex and more time intensive work within existing staffing and resource capability.
The algorithms developed and implemented by LCR 4.0 and the VEC bring AlgoLib’s existing solutions to the next level. Shortly after the current project, a series of new projects are proposed by AlgoLib as continuations.
AlgoLib are looking for further and deeper collaborations on data processing, model simulation and cloud computing, bringing intelligent data-driven solutions with innovative technology to help businesses in the Liverpool City Region and throughout the UK. Also, with the solutions supplied to AlgoLib, they are looking at a higher market valuation with a promising seed funding opportunities.
“The support we received from LCR 4.0 and the VEC helped us to protect our intellectual assets and identify legal, risk and commercial concerns.
It also brought us more business, development and funding opportunities. These solid deliveries have enabled us to fulfil our client demands in a whole new level we could not have imaged before. Thanks to the tech and legal supports from VEC, we now have 500 community members and hosted 20 algorithms developing seminars across UK. Our turnover is passing £100k in the first year”
– Simon Wang, Owner and Founder of AlgoLib.
For the downloadable Case Study: AlgoLib