Talentli. Merit-based scoring system
Background
Working as a recruiter, I learned that traditional platforms were ineffective in finding the best talent. Most online services, including LinkedIn, are relevance-based. They show people through keywords, not real ability. I wanted to change that. After a long research, in 2021 I developed Talentli, a merit-based system that scores every experience and achievement of a person, then ranks people by their score. This made it possible to instantly find the best professionals in any industry or the most promising students in any school.
I led the project from the start. Conducted market analysis and customer development with both users and companies. Created the scoring system, built a complex database directly connected to the scoring system, and designed the entire user interface. Developed the MVP and brought in the first users and customers. Beyond product work, I joined the best local startup incubator, became a finalist, and won a grant. Participated in YC Startup School and Microsoft Founders Hub. Built financial models, pitched to VCs, and handled all external communications. To prepare for growth, I incorporated the company in Delaware.
Talentli was a direct extension of my previous work on Talent Network and Everythink. You can read about them on the corresponding pages (links provided below).
Outcome
Unfortunately, the funding was limited, and I stopped the project after 2 years. But it doesn't mean that I have closed the project. Talentli is still up to 50 times more efficient than existing relevance-based system. Through this experience, I proved that merit-based systems can redefine people search, and I built the foundation for scaling Talentli further.

Pitchdeck






Teammates (in ABC order)
Denis Mukushev
Related
Talent Network. Community of talented students
Everythink. Landing page for the recruitment platform
Talentli. Landing page for the merit-based scoring system
Talentli. UI for the merit-based scoring system
Merit-based scoring systems as objective alternative to search people, Paper (currently in development)