This is a topic that definitely deserves maximum attention! The Avatr DApp will be built to thrive off data, from the dynamic rating and reward systems, the AI and machine learning enhanced search functionality, on-chain analytics, and the dynamic upskilling capability. Data optimization will create an environment where the greater the aggregate of information, the more dependent the system performance.

Let’s get started…let’s imagine the database is populated with Avatrs, representing both employers and candidates. The employment market is now tokenized, and we are preparing for business. When an employer posts an intra-system request, the job description and proposed rates and conditions will be uploaded, which would activate the built-in search function. No third-party advertising required.

The AI enabled database, pre-populated with a range of skills and qualifications, would enable refined searches on a comprehensive range of metrics including but not limited to:

• Demonstrated skills

• Certifications/tickets/degrees

• Desired pay & conditions

• Location (if any)

• Job Duration

• Candidate ratings & APC (Avatr Performance Co-efficient)

• A suite of on-chain analytics

The parameter-based search would then nominate suitable candidates in the database to be notified of preselection. Via an accept/decline function, candidates could elect to either progress or decline the assignment.

Alternatively, candidates could respond directly to uploaded job postings, the success of which would still be determined by the pre-selection engine. From here a short list would be developed and made visible to the client who could either interview via an in-house function or progress to the hiring process, whatever their preference.

When both parties are agreed, a smart contract would then be generated between the parties, and work allowed to commence pending compliance requirements.

The distinguishing feature of a singular/common platform is that it allows for frictionless, efficient, and seamless interaction. We eliminate the risk of creating applicant silos that result from multiple data collection points as is the case in the legacy system. Furthermore, the scattergun advertising approach that invariably yields unsuitable applicants is also eliminated.

This is a topic that definitely deserves maximum attention! The Avatr DApp will be built to thrive off data, from the dynamic rating and reward systems, the AI and machine learning enhanced search functionality, on-chain analytics, and the dynamic upskilling capability. Data optimization will create an environment where the greater the aggregate of information, the more dependent the system performance.

Let’s get started…let’s imagine the database is populated with Avatrs, representing both employers and candidates. The employment market is now tokenized, and we are preparing for business. When an employer posts an intra-system request, the job description and proposed rates and conditions will be uploaded, which would activate the built-in search function. No third-party advertising required.

The AI enabled database, pre-populated with a range of skills and qualifications, would enable refined searches on a comprehensive range of metrics including but not limited to:

• Demonstrated skills

• Certifications/tickets/degrees

• Desired pay & conditions

• Location (if any)

• Job Duration

• Candidate ratings & APC (Avatr Performance Co-efficient)

• A suite of on-chain analytics

The parameter-based search would then nominate suitable candidates in the database to be notified of preselection. Via an accept/decline function, candidates could elect to either progress or decline the assignment.

Alternatively, candidates could respond directly to uploaded job postings, the success of which would still be determined by the pre-selection engine. From here a short list would be developed and made visible to the client who could either interview via an in-house function or progress to the hiring process, whatever their preference.

When both parties are agreed, a smart contract would then be generated between the parties, and work allowed to commence pending compliance requirements.

The distinguishing feature of a singular/common platform is that it allows for frictionless, efficient, and seamless interaction. We eliminate the risk of creating applicant silos that result from multiple data collection points as is the case in the legacy system. Furthermore, the scattergun advertising approach that invariably yields unsuitable applicants is also eliminated.