How Mentora works
From your answer to your recommendation
We show you how your answers turn into clear recommendations, and how we interpret the result and keep reviewing its quality.
A simple explanation of the mechanism, without equations or complex details.
Illustrative example
- Your answers
- A pattern forms
- Compared with paths
Explained recommendations
Data Analyst
It appeared because more than one signal in your answers related to the nature of this path.
How the result comes together
Every answer adds a piece of the picture. As you move through the analysis, the pattern starts to clear and the paths closest to you appear.
You answer the questions
We turn your answers into signals
We gather the signals into one picture
We compare them with the work in each path
We rank the closest and explain why
Illustrative example
Why might data analysis appear?
This illustrates a group of signals related to the work. It is not your personal result.
Data Analyst
Checking information before concluding
In this example, answers suggest a preference for gathering and comparing information before reaching a conclusion.
Looking for relationships in results
An interest in causes and recurring patterns relates to part of the work in data analysis.
Breaking a problem into parts
Reviewing a problem step by step is a preference related to analytical tasks.
Organizing information before interpreting it
Organizing and connecting data is part of the work, and needs practical experience to verify.
Explaining results to a team
The role includes communicating findings clearly. An interest in that role does not mean presentation skill was measured by the assessment.
How to read this example
A field appears through several signals together. One answer resembling one job task is not enough to establish suitability.
Questions to explore in practice
- Do you enjoy long stretches of work with data?
- Does sustained focus on analysis suit you?
- Are you willing to learn the basic tools?
What the result means
A result has three parts
Work-style closeness
It compares your answer pattern with the nature of the work in a path.
How recommendations rank
It ranks paths on a set of signals, not on a single question.
The result doesn’t choose for you
It gives you a clearer starting point; the final decision stays yours.
The limits of the result
- We don’t give you more confidence than we have
- We don’t ignore conflicts in your answers
- We don’t raise a result when the signals aren’t enough
- When paths are close, we say so clearly
The market’s role in ranking
Market status shows up as a separate indicator that helps you compare, but on its own it doesn’t decide the best path for you. The figures are estimates and can vary by city and sector.
The techniques behind a recommendation
We explain the general idea of each technique, without revealing the formulas or internal details.
- The next question changes with your answers
- Adaptive question selection
- Instead of a fixed list, the system picks the question that adds the most information at that moment.
- The picture speaks after every answer
- Bayesian updating
- Each answer re-ranks the possibilities, so the paths become clearer gradually during the analysis.
- We compare your pattern with the work itself
- Profile matching
- We gather signals from your answers, then compare them with the way of working tied to each path.
- We separate path fit from market status
- Fit vs. market
- How close a path is to your answers is one thing; its market status is another. We show them separately so the difference is clear.
How we review quality
The review repeats, looping back to the first step
We review the clarity of the question
We test the effect of any change
We review the reason behind a recommendation
We adopt the update after comparing
We don’t adopt a change just because it looks better. We compare it with the previous version and review its effect on answers and recommendations.
Ready to see your result?
Answer honestly, and take the recommendations as a starting point for research and comparison.