Naomi AI is the heart of the NaomiHire Recruiting Career Service.

The main scientific issue was how to evaluate a distance between any two concepts where a concept is anything that can be put into a taxonomy or ontology context. For the purpose/goal of the issue a distance (distance function) between two concepts means a strong mathematical metric. The answer to the main question has been given in the paper “MODEL OF COMPUTATIONS OVER CLASSIFICATIONS”

But no reasons to think that a mind uses only one simple metric for evaluating distance in the issues of comparison two concepts. List of distance functions between two concepts were represented in “TRAINABLE MODEL OF THE CALCULUS OVER CLASSIFICATIONS”

In the same paper a learnable model over classifications(ontologies) has been described and explained.  

Using mathematical metrics is possible only if a context is a metric space. It required to define some rules how to create taxonomy/ontology in a way to guarantee that the result will be a metric space. The results are presented in the paper “CLASSIFICATION CALCULUS. THE CLASSIFICATION CORRECTNESS”

During building ontologies for NaomiHire the co-founders validated results of each other in accordance with the rules “MEASURE OF DIFFERENCE BETWEEN CLASSIFICATIONS”

The general approach used for matching a candidate and a job description was published in “THE CALCULUS OVER CLASSIFICATIONS. SELECTION OF PERSONNEL AS INTERPRETATION OF THE PROBLEM OF EXPERT SELECTION”

Naomi AI uses a set of specific rules and applies them in accordance to feedforward neural network output for explaining its decisions.


The main goal of the testing was to identify how Naomi explains its decisions regarding to:

  1. a) recruiting the best candidates for a particular job;
  2. b) sourcing the best opportunity for a particular candidate.

The testing process was built on real job descriptions and real candidates. We use significant computer ontologies to build job descriptions and candidates profiles: locations (> 77K classes), soft skills ( > 300 classes), hard skills (>8K classes), industries (260 classes), natural languages (> 800 classes). NaomiHire Team conducted a blind testing session with real companies and real candidates and have got accuracy 92,3% of best candidates selected for a specific job. We also have comparable accuracy (>90%) when we find the best job for the specific candidate. The details are available in our previous post “NAOMIHIRE: AI CAN EXPLAIN ITS DECISIONS OR RECRUITING WITHOUT PAIN”   

The most often asked question is why NaomiHire team considers that it uses exactly AI but not machine learning. The answer is given on the header image that presents AI scope where following functionalities are provided by NaomiHire exclusively in the automation (machine) mode:

  • Data filtering
  • Data normalization and conceptualization
  • Ensemble methods or models
  • Insights value assessment and interpretation
  • Decision variants and hypothesis producing
  • Deduction or hypothesis pre-validation and
  • Reflection

It means that Naomi today corresponds to weak artificial intelligence and in the next decade will become a strong AI

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