Most matching approaches use regressive, top-down pairing — great for a lucky few, bad for most. Mentorloop takes a fundamentally different approach: matching the entire cohort at once, so everyone gets a high-quality connection.
96%
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150k+
Regressive matching is a flawed, top-down pairing process where the first participants matched receive the best, most compatible mentors, while everyone else receives increasingly poor matches. It’s the most common problem in mentoring programs — and most coordinators don’t even know it’s happening.
Equitable matching evaluates every possible pairing across an entire cohort simultaneously, then selects the configuration that produces the strongest matches for everyone — not just the first few participants. Mentorloop is the only mentoring platform that uses this approach.
For a cohort of 100 participants, there are 4,950 unique possible pairings. At 200 participants, that rises to steeply to 19,900. At 1,000 participants, the Mentorloop algorithm evaluates nearly 500,000 possible pairs, and finds the optimal configuration in just seconds.
Mentorloop is the only mentoring platform with an equitable matching algorithm, built on a decade of real mentoring data, now enhanced with AI.
No regressive matching. No one left behind.
Matching isn’t a one-pair problem, it’s an optimisation challenge across hundreds or thousands of relationships. Any tool that matches participants sequentially produces regressive results.
Mentorloop’s Smart Match algorithm is built on over a decade of real mentoring data. It now uses AI to understand free-text responses — bios, goals, expectations, and aspirations — not just dropdown selections. This means matches are based on who people really are, not just the boxes they ticked.
Life-changing connections, made in minutes
With three key matching approaches, all supported by Mentorloop’s unique equitable matching algorithm,
you can make mentoring magic happen with ease.
There are three main approaches: algorithmic smart matching, participant-led self matching, and coordinator-led manual matching. The best mentoring programs layer these together. All three should be built on equitable foundations to avoid regressive matching.
Match your participants in a matter of seconds
Algorithmic cohort matching. Set your criteria, let the algorithm evaluate every possible pairing, and review draft matches before they go live. Now AI-enhanced to understand free-text fields like bios and goals.
Best for
Large programs where fairness, speed, and scale matter.
Give your participants the power to make connections
Participants browse profiles and request their own matches — supported by algorithm-generated Recommended Matches to reduce unconscious bias and combat regressive self-selection.
Best for
Open, always-on mentoring networks and building Personal Advisory Boards.
Pair your people up into life-changing connections
Browse your cohort with compatibility insights and Recommended Matches — all the data you need to make informed pairings without unconscious bias driving outcomes.
Best for
Leadership programs, high-potential cohorts, programs with sponsor requirements.
Self Match on Mentorloop is supported by Recommended Matches – match suggestions made by the algorithm based on criteria that appear on your participants’ profiles (goals, skills, interests, etc).
Recommended Matches help your participants make informed decisions and reduce unconscious bias.
With Mentorloop, matching 500+ participants takes minutes rather than days or weeks. Customers report reducing matching time from 8–9 working days to a few hours, with 96% match satisfaction and over 250 hours of admin time saved.
250+
96%
Minutes
“Smart Match has saved us a tremendous amount of time – estimated at over 250+ hours. Time that would have otherwise been spent on categorising, matching, managing and reporting!”
Senior Culture and People Partner at Woolworths Group
“PC Smart Match is game changing — it gives them the flexibility they need for matching. This has allowed them to match everyone, which is something they’ve not been able to do easily before.”
Mentoring Programme Manager Cherie Blair Foundation for Women
Regressive matching is a flawed, top-down pairing process in which the highest-scoring participants at the top of a list receive the best, most compatible mentors. In contrast, those further down receive increasingly poor matches. This creates a ‘rich get richer’ dynamic — early pairs get near-perfect matches while later pairs get whoever is left. Most mentoring platforms, spreadsheets, and sequential AI tools produce regressive matching results.
Equitable matching evaluates every possible pairing across an entire cohort simultaneously, then selects the configuration that produces the strongest matches for everyone — not just the first few participants. Unlike regressive matching, equitable matching ensures that match quality stays consistently high across the whole program. Mentorloop is the only mentoring platform with an equitable matching algorithm.
Mentoring matching software automates the process of pairing mentors and mentees based on criteria such as skills, goals, experience level, location, and interests. Participants complete profiles, and the software uses algorithms to generate optimal pairings. The best matching software evaluates all possible combinations across the entire cohort simultaneously, rather than matching participants one at a time.
Matching mentors and mentees at scale requires algorithmic matching rather than manual spreadsheets. For a cohort of 1,000 participants, there are nearly 500,000 possible unique pairings to consider. Software like Mentorloop uses an equitable matching algorithm that evaluates all possible pairs simultaneously and creates draft matches in minutes. Customers report reducing matching time from 8–9 working days to a few hours, with 96% match satisfaction.
With mentoring matching software like Mentorloop, matching 500+ participants takes minutes rather than days or weeks. Customers report reducing matching time from 8–9 working days to 1 hour. One customer estimated saving over 250 hours of administrative time.
Smart matching uses algorithms to evaluate all possible pairings across a cohort and generate optimal draft matches for coordinator review. Self-matching allows participants to browse profiles and request their own matches, supported by algorithm-generated recommendations. Manual matching gives coordinators full control to create curated pairings. The three approaches can be layered together.
To reduce bias in mentor matching, use algorithmic matching rather than manual pairing. Manual matching is prone to unconscious bias. Equitable algorithmic matching removes this by evaluating all participants against objective criteria simultaneously. Features like Recommended Matches and AI-powered free-text analysis further reduce bias by surfacing alignment that human reviewers might overlook.
The best way to match mentors and mentees is to use an equitable algorithmic approach (like that of Mentorloop) that considers the entire cohort at once, rather than pairing participants one at a time. This prevents regressive matching, where early pairs get the best matches, and later pairs get the worst. Look for matching software that evaluates skills, goals, experience, and free-text responses; reduces unconscious bias; and gives program coordinators the ability to review and approve matches before they go live.
You’re only four steps away.
Explore your program in demo mode, and don’t pay a penny until you’re ready to invite your mentors and mentees.
