Beginning in 2024, our startup has been successfully building its user base in Central Europe (Poland, Slovenia, Romania, Czech Republic, Slovakia). Within the next five years, we intend to obtain at least 2,000 major B2B partners, as well as no less than 100,000 individual clients (B2C).

 

At the present stage, we organize pilots within the mobility industry (2024/2025) — targeting to acquire key B2B customers in Europe. Our solution is delivered directly to its B2B customers (D2C). It will be also offered in Google/Apple application stores for individual clients. The second phase of our development includes the expansion to East Asia (2025/2026), as well as strengthening our business position in the EU and US.

 

Afterwards, we will bring our technology to the B2B user communities in India (2026/2027) and Brazil (2027/2028), consequently entering the most promising markets in terms of early adopters. We have successfully initiated the process of building strategic connections across all of these markets, aiming to turn TCR App into a global and universal solution for road traffic accident prevention. Concurrently, the supplementary modules of TCR App (medical diagnostics, advisory services) are offered through distinctive subscription-based business models.

At the moment, scaling to East Asia is a top business priority for our startup (2nd part of 2025, 2026). We are both actively fundraising and establishing future partnerships in order to introduce our product to representatives of Asian mobility, transport and healthcare industries.

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In the European Union, the total number of our potential major early adopters exceeds 80,000 entities. Our market analysis has shown that at least 22% of the TCR App’s recipients are expected to pay for our services. We project annual recurring revenue of €2.2 million in 2027, €15 million in 2029 and €95 million in 2031.

 

The fundamental differences between our startup and its potential competitors stem from the fact that our technology: [1] is based on unique scientific research with pioneering findings about human cognition, [2] incorporates data segments that are unavailable to any other entity (including these registered outside of the vehicle) and [3] employs the AI-driven approach (e.g., deep neural networks) to the prediction of abrupt attention lapses (a distinctive phenomenon in terms of origin) in a thoroughly personalized way. We address our solution to everyone, not only to the richest.