To make our project as advanced and user-friendly as possible, we utilize cutting-edge technologies such as blockchain, AI, and P2P. But how will our platform work? Let’s break it down!
Avatr DApp is designed to thrive on data, incorporating dynamic rating and reward systems, AI and machine learning-enhanced search functionality, on-chain analytics, and dynamic upskilling capabilities. Data optimization will establish an environment where the system’s performance becomes more dependable as the aggregate of information increases.
Imagine a database populated with Avatrs representing both employers and candidates. The job market is now tokenized, and we are gearing up for business. When an employer posts a request within the system, including the job description, proposed rates, and conditions, it activates the built-in search function without the need for third-party advertising.
The AI-enabled database, pre-filled with a variety of skills and qualifications, enables refined searches based on metrics such as:
- Demonstrated skills
- Certifications/tickets/degrees
- Desired pay & conditions
- Location (if any)
- Job duration
- Candidate ratings & APC (Avatr Performance Co-efficient)
- On-chain analytics suite
The parameter-based search then nominates suitable candidates in the database to be notified of preselection. Using an accept/decline function, candidates can choose to either progress or decline the assignment.
Alternatively, candidates can directly respond to uploaded job postings, with the success determined by the pre-selection engine. A shortlist is then developed and made visible to the client, who can either conduct interviews via an in-house function or proceed to the hiring process according to their preference.
Once both parties agree, a smart contract is generated between them, and work can commence pending compliance requirements.
The distinctive feature of a unified platform is its ability to facilitate frictionless, efficient, and seamless interaction. This eliminates the risk of creating applicant silos present in legacy systems with multiple data collection points. Additionally, the scattergun advertising approach, which often yields unsuitable applicants, is eliminated as job posts are generated internally. The system’s pre-selection function produces refined and accurate candidate lists, significantly expediting the hiring process and eliminating friction points.
Do you think such functionality will be in demand among our platform participants? Share your opinion in the comments!
