Technology

Vettd’s technology brings a new innovation in data science to the hiring process. After years in development, our machine discovery technology has allowed small, medium, and enterprise businesses to streamline their decision making process.
Our technology is purpose-built to enable any person at any company to make informed hiring decisions regardless of hiring expertise or experience. The application of this within a hiring organization leads to significant time savings, repeatable hiring success, and countless additional efficiencies.

How it helps.

The technology behind Vettd is built specifically for the purpose of making hiring more simple. The amount of time and knowledge necessary to properly prioritize a candidate pool is hard to come by. Our technology analyzes and compiles information on your behalf to save you time, effort, and unnecessary frustration.

Without Vettd

A typical resume review process is disorganized in that resumes are looked at in some arbitrary order and there is no sophisticated categorization. Time is spent reviewing every resume regardless of their quality.

With Vettd

With Vettd, a resume review process begins with reviewing the most relevant resume first. Each resume has a letter grade that reveals it’s rank in comparison to the rest of the group. This allows the reviewer to focus solely on the most relevant candidates cutting review time drastically.

How it works.

Vettd uses a new idea in data science called, machine discovery. This allows us to learn the full context of each job extemporaneously as we compare resumes against a given job description. No initial setup or ongoing, tedious training is needed from the user.

1. Analyze job description

The first step in Vettd’s machine discovery process is to analyze the job description to setup the context for comparison. In this step, Vettd establishes a set of learned semantic descriptors as the attributes for comparison.

2. Assign resume relevance

The next step is to compare a set of candidate resumes to the descriptors from step one. Thousands of data points are analyzed and compared here with the output being a simple letter grade distribution.