Utilizing OpenAI, MentorEase can extract, classify, and understand free-form text that mentors and mentees enter in registration forms and resume uploads.

This means that when a block of text is entered into a text box – AI is able to read it, identify common phrases the mentor and mentee used, and then add it into the matching algorithm mix.

Beyond this, MentorEase can use AI tools to automatically review all mentor and mentee resumes and CVs to find similar terms that they both use. The system can review thousands of words instantly, extracting key terms and important ‘needles in a haystack’ – terms and phrases that both the mentee and potential mentors used.

MentorEase shows these key terms in context side by side so they can be assessed by the mentee or admin selecting a match. This pattern recognition service provided by AI can be very useful and practically impossible to find without many hours of comparing resumes.

AI integrations can include:

AI identifies sentences and phrases that capture the main ideas of a paragraph or document. These insights can then be added to other matching criteria.

AI can detect and connect important concepts and key phrases in text – including named people, locations, events, industries, or organizations.

Using a dictionary of positive, negative, and neutral words, the system analyzes overall sentiment. For example, if a mentee writes: “I’m struggling to find the confidence to present to a group – it’s something I find quite intimidating” and a mentor has experience in public speaking or presentation coaching, the system can flag that as a possible match.

To improve relevance, AI can be trained to ignore common but unhelpful words or phrases that may appear in the text but don’t contribute to effective matching.

This process merges similar words (like “manage,” “managing,” “management”) so the AI groups them together, reducing clutter and improving accuracy.

This technique converts the content of resumes and text fields into mathematical descriptions. Each profile is then mapped in a vector space, and the distance”between mentor and mentee profiles can be calculated to find the best potential matches.

By adding this new tool for matching we can now display three scores for each potential match:

* Field Score – from matching key data fields such as selections, checkboxes, etc.
* AI Score – from the open text fields and resume comparisons by AI
* Overall Score – combine scores of both

When reviewing potential matches MentorEase allows filtering by each of these scores to obtain a better idea of the best possible match for each mentee.