AVT DApp Recruitment Mechanics

The AVT engine room 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 dependable the system performance.

Let’s get started…let’s imagine the data base is populated with AVT, 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 data base, 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 (AVT 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. Job posts will be generated internally. The system’s pre-selection function will produce refined and accurate candidate lists which will speed the process exponentially. Meaningful shortlists will be created at the click of a button.

Dynamic and Targeted Up-Skilling

Job applications often discouragingly disappear into an abyss. The legacy system often sees applicants left uncontacted when deemed unsuitable (or missed), depriving that candidate of any direction. Our objective is to assist unsuccessful candidates by presenting them with a positive path forward. They will be provided specifics as to what impeded their progression via our system recognition and matching metrics. In the event of a skill deficiency, gap training will be offered. Whilst it is beneficial to be offered training, of more significance it must be outcome oriented. Course recommendations and providers will be community driven. Employers have a vested interest in the quality of their talent pool, which will ensure that training recommendations are 100% relevant and match job requirements. Information will be made available to the community in real time, taking guesswork out of upskilling process. Every unsuccessful application will now create an opportunity for targeted upskilling. Real time gap analysis will add true value and turbo boost our objectives. What was once a dead end for applicants will now represent a step forward. Amassing an authentic, user-based repository of training recommendations over time will provide an extensive guidance tool for the workplace community.