Periodic Reporting for period 1 - Flaner (Flaner, a memory enhancement software platform)
Période du rapport: 2018-12-01 au 2019-05-31
Powered by a strong technology backbone based on information retrieval, natural language processing, machine learning, neural networks and deep learning, Flaner understands and extracts the relevant information from a given content, continuously enhances it and - using context identification and recommendation systems - automatically and proactively brings back memory insights notifying users whenever these are most valuable to them. Notably, Flaner acts as a digital extension of our memory.
As this problem is enormous and execution focus is paramount, the company has decided to tackle one vertical at a time and initially help users to memorize content around places, targeting people who are interested in exploring their own cities or travelling to different ones. Flaner’s memory enhancement technology focused on places is inherently a global product, while also allowing for future expansion to different verticals.
Flaner connects users with content creators, meaning traditional publishers, bloggers and influencers, giving them tools to better engage their audience, reducing time spent to create content, successfully transition from desktop to mobile and grow their monetisation.
Having confirmed the market acceptance and market potential of Flaner’s memory enhancement technology, as well as received promising results with the feasibility study carried out under the Phase 1 project, we plan on extending them over the EIC Accelerator Pilot. In fact, with the help of the EIC Accelerator Pilot funding, we will finalize the development and testing of Flaner’s memory enhancement platform for end users and content creators and later on launch it in multiple European and global markets.
Technological assessment: Through user tests and content creator’s pilots, we gathered additional information on the technical feasibility of our core innovations. The feasibility of our technology platform was tested under several real case scenarios and, at this moment, our machine learning algorithms have outperformed some of the most respected tech companies focused on Artificial Intelligence in the industry. The collected pilot data and user experience reports provided us with sufficient data to create a development plan towards market readiness.
Commercial and market assessment: we conducted in-depth analyses of the initial target markets to realistically evaluate market size, growth, competitors/substitutes, clients, and partners. This helped us to determine the likelihood of market success, plan next steps, while simultaneously assessing acceptance of our product among customers groups. Flaner will be initially focused on places and their explorers; thus, the market segmentation for end users will be done according to a combination of (i) geography, (ii) age and (iii) income. Regarding content creators, the market segmentation will be based on three main criteria: i) geography, ii) creator’s audience size and iii) type of content. Notably, our business model is based on a travel content marketplace where we connect both end users and content creators.
IPR assessment: Flaner LDA is the full owner of all intellectual property involved in its product and processes. We conducted a preliminary freedom to operate analysis in-house and have determined that there are no barriers to full commercialisation. There are no known regulatory issues preventing the company from operating. The product does not infringe on any known patents. We face no specific regulatory barriers, but the issue of data protection is important. We cooperate closely with our customers and we have extensive experience with privacy policy issues, therefore, we are making sure the product and services are fully GDPR compliant. As Flaner’s memory enhancement software platform is planned to be a global product, the team has already registered the brand and domain names on various territories, taking into consideration the expansion strategy.
If we compare Flaner with the current city and travel apps, we can see notable differences in terms of two main criteria: Content and User Experience. In terms of content, a historical issue for travel tech apps, most available solutions work only as a places database, giving users access to user-generated content (UGC) only and no curated content. Flaner, on the other hand, relies on its information retrieval and natural language processing technologies to automatically offer quality curated content already available from a variety of publishers, bloggers and influencers meaning a very scalable solution. User experience wise, some available products do not offer much more than browsing through places bookmarks and guides while others currently allow to manually build maps and better organize content. Flaner takes user experience to a whole new level as it provides a seamless tool to extract the relevant places from any content the user chooses to memorize and is able to automatically retrieve them at the right moment and at the right context for the user - seamlessly and effortlessly.