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Our technology is built on three whales: computer ontology (CA),
analytic hierarchy process
(AHP) and artificial intelligence (AI).
An ontology is a formal description of knowledge as a set of concepts within a domain and the relationships that hold between them. To enable such a description, we need to formally specify components such as individuals (instances of objects), classes, attributes and relations as well as restrictions, rules and axioms. As a result, ontologies do not only introduce a sharable and reusable knowledge representation but can also add new knowledge about the domain [
]. A computer ontology (CA) is an ontology readable by a computer.
Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry [
The Analytic Hierarchy Process (AHP) is a general theory of measurement. It is used to derive
ratio scales from both discrete and continuous paired comparisons. These comparisons may be
taken from actual measurements or from a fundamental scale which reflects the relative strength of preferences and feelings. The AHP has a special concern with departure from consistency, its measurement and on dependence within and between the groups of elements of its structure. It has found its widest applications in multicriteria decision making, planning and resource allocation and in conflict resolution [
CA brings a conceptualization of the skills and lets us use meaning instead of words. It means that our AI “know” how is similar (or different) skills each to other, for example:
Hiring candidates and Candidates onboarding;
Woodturner skill and Metalturner skill.
Thinking more globally, it gives the answer to the question of how is similar apple and orange.
Naomi uses ontologies to find a more relevant employee for occupation based on skills and skill requirements and evaluate the skill gap of a particular employee for the occupation.
AHP helps Naomi to build correct goal functions for selecting the right employees for occupations to minimize total skill gap of a company/
Deep learning as a part of AI helps to find hidden correlations and, as a result, hidden skills. The machine learning approaches as a part of Nami gives an understanding of performance managers biases, learning curves for every employee and hidden vector of employee development.
This joining of technologies helps us to:
forget about job titles and be focused on skills;
evaluate momentum inner price of a particular skill inside a company;
analyze trends of the inner price of sills, inner skill demand and skill supply;
represent a skillset as a capital where every skill has momentum inner price;
evaluate the indicative inner price for the set of skill requirements;
find a skill learning curve for every employee;
estimate return of investment of employee development in a particular skill.
get a precise analysis of employee, department, project or company by more 30 metrics
Naomi helps you to think about your workforce as about human capital in the same way as a financier thinks about financial capital.
Meet a true deep tech with Naomi!