The European Investment Bank (EIB) is investing €20 million in the Portuguese software company Bizay to finance the implementation of its research and development (R&D) programme and product development roadmap.
One of Bizay's main focuses will be its tech-based B2B marketplace for customised products targeted at small and medium-sized enterprises (SMEs), such as retail stores, restaurants, hotels and small corporates. This financing will also promote job creation in Portugal, a fundamental aspect for the European Union's post-COVID-19 economic recovery.
The agreement was announced at Web Summit by EIB Vice-President Ricardo Mourinho Félix and Bizay CGO José Salgado.
This financing – in line with the European Commission's strategy to strengthen competitiveness and innovation in digital technologies – will further support Bizay's overall growth and geographic expansion plan and aims to create over 120 jobs in fields related to technology and business development over the next four years in Portugal.
Bizay's marketplace, available in over 20 countries spanning from Europe to Latin America, offers a wide catalogue of customisable products, ranging from corporate gifts and promotional products to packaging, business cards and leaflets across all verticals.
Thanks to the EU bank's support, the company will further digitalise every step of the supply chain to make it more cost and time efficient. It uses artificial intelligence technologies like state-of-the-art machine learning algorithms for automating online marketing bidding processes, thereby improving overall marketing efficiency. Furthermore, Bizay's business model enables an entire ecosystem of small businesses and manufacturing companies to connect and benefit from each other, giving them access to a wide catalogue of customisable products at a competitive price, which are otherwise very expensive for small businesses, and with a short delivery time.
The EIB will further support Bizay's current technology developments, which include initiatives that will make use of learning models to forecast future traffic loads and of artificial intelligence to improve order aggregation and further improve production cost efficiency.