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Matching algorithm & rules: Every pair has
a reason to meet.

Tell RandomCoffee what makes a good match for your program. It pairs hundreds of people in seconds, avoids repeat matches, and shows you why each pair works.

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Ranked rules on any profile field Meeting history across programs Review before sending Pairs, groups or mentoring
Coffee chats · Round 12 Autoplay · drag a rule to take control
Matches 20 members · 10 pairs
DR David Renaud Sales · SF JB Jordan Blake Product · SF 93% match
Matching rules quality
  • ✓ Never met before
  • ✓ Department · Is different
  • ✓ Office · Is the closest
  • ✓ Seniority · Is the furthest
  • ✗ Skills · Has the most in common
ML Marcus Lee Sales · Chicago AR Alex Renard Finance · Chicago 93% match
Matching rules quality
  • ✓ Never met before
  • ✓ Department · Is different
  • ✓ Office · Is the closest
  • ~ Seniority · Is the furthest
  • ~ Skills · Has the most in common
EK Elena Kova Engineering · NYC NP Nina Patel Sales · NYC 87% match
Matching rules quality
  • ✓ Never met before
  • ✓ Department · Is different
  • ✓ Office · Is the closest
  • ~ Seniority · Is the furthest
  • ✗ Skills · Has the most in common
JP Julien Prot Sales · NYC CD Chloé Dubois Finance · NYC 88% match
Matching rules quality
  • ✓ Never met before
  • ✓ Department · Is different
  • ✓ Office · Is the closest
  • ~ Seniority · Is the furthest
  • ✗ Skills · Has the most in common
AM Alice Martin Marketing · SF OH Omar Haddad Finance · SF 87% match
Matching rules quality
  • ✓ Never met before
  • ✓ Department · Is different
  • ✓ Office · Is the closest
  • ~ Seniority · Is the furthest
  • ✗ Skills · Has the most in common
LR Lucia Romero Marketing · Chicago PS Priya Shah Engineering · Chicago 87% match
Matching rules quality
  • ✓ Never met before
  • ✓ Department · Is different
  • ✓ Office · Is the closest
  • ~ Seniority · Is the furthest
  • ✗ Skills · Has the most in common
MT Maya Thomas Engineering · SF SN Sara Nguyen Product · SF 86% match
Matching rules quality
  • ✓ Never met before
  • ✓ Department · Is different
  • ✓ Office · Is the closest
  • ~ Seniority · Is the furthest
  • ~ Skills · Has the most in common
BK Bob Kim Marketing · NYC HB Hannah Brooks Product · NYC 90% match
Matching rules quality
  • ✓ Never met before
  • ✓ Department · Is different
  • ✓ Office · Is the closest
  • ~ Seniority · Is the furthest
  • ~ Skills · Has the most in common
TG Thomas Girard Finance · NYC SC Sam Carter Marketing · NYC 83% match
Matching rules quality
  • ✓ Never met before
  • ✓ Department · Is different
  • ✓ Office · Is the closest
  • ~ Seniority · Is the furthest
  • ~ Skills · Has the most in common
CL Claire Loeuvre Product · Chicago EP Ethan Park Engineering · SF 73% match
Matching rules quality
  • ✓ Never met before
  • ✓ Department · Is different
  • ~ Office · Is the closest
  • ~ Seniority · Is the furthest
  • ~ Skills · Has the most in common

Trusted by 1,250+ organizations across more than 30 countries:

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Sound familiar?

A bad match costs more than one awkward coffee.

People who don’t click don’t come back, and the whole program loses its credibility.

Today

“I got matched with Julien. Again.”

The same faces come back every round, and people stop signing up for something that feels random.

With RandomCoffee

Every round checks the meeting history of all your programs first, so people meet someone new.

Meeting history
Today

“Pairing 300 people takes me two days.”

A spreadsheet, a dozen filters and a lot of guesswork, and it all starts over next month.

With RandomCoffee

Rank your rules once. Every round is generated in seconds, ready for you to review.

Matching rules
Today

“My mentor can’t help with what I need.”

Mentors are assigned by availability, not by what the mentee actually wants to learn.

With RandomCoffee

Match mentor skills with mentee goals, so every pair starts with something to work on.

Category overlap
One engine, every program

Different goals. The right pairs for each.

Mix teams in coffee chats, keep buddies close to their new hire, and give every mentee a mentor with the skills they want to learn.

Matching rules
1 History Never matched before
2 Department Is different

Every round builds bridges between teams that never talk.

MT Maya Thomas Engineering · San Francisco JP Julien Prot Sales · New York Engineering × SalesFirst meeting
BK Bob Kim Marketing · New York TG Thomas Girard Finance · New York Marketing × FinanceFirst meeting
CL Claire Loeuvre Product · Chicago EP Ethan Park Engineering · San Francisco Product × EngineeringFirst meeting
HB Hannah Brooks Product · New York NP Nina Patel Sales · New York Product × SalesFirst meeting
Matching rules
1 Office Is the closest
2 Department Is different

Every new hire gets a buddy nearby, outside their own team.

EK Elena Kova Engineering · New York HB Hannah Brooks Product · New York Same office · New YorkAnother team
ML Marcus Lee Sales · Chicago PS Priya Shah Engineering · Chicago Same office · ChicagoAnother team
JB Jordan Blake Product · San Francisco AM Alice Martin Marketing · San Francisco Same office · San FranciscoAnother team
OH Omar Haddad Finance · San Francisco SN Sara Nguyen Product · San Francisco Same office · San FranciscoAnother team
Matching rules
1 Mentor skills ↔ mentee goals Maximize overlap
2 Department Is different

Every mentee learns from someone who has the skills they want.

EK Elena Kova Engineering · New York LR Lucia Romero Marketing · Chicago LeadershipPublic speaking
ML Marcus Lee Sales · Chicago MT Maya Thomas Engineering · San Francisco Data vizPython
JB Jordan Blake Product · San Francisco DR David Renaud Sales · San Francisco NegotiationLeadership
OH Omar Haddad Finance · San Francisco HB Hannah Brooks Product · New York DesignLeadership
Matching rules

Your definition of a good match, in a few rules.

Combine as many rules as you need and rank them by importance. No spreadsheet, no formula: the order is the strategy.

  • Drag rules to set their priority: the top rule always wins
  • Match on text, lists, dates or numbers, from your HRIS, forms or audiences
  • The same rules run every round, automatically
1

Category

Bring people together or apart on any profile field: text, list, date, number, choice or yes/no.

  • Is the same
  • Is different
  • Is the closest
  • Is the furthest
  • Has the most in common
  • Has the least in common

Start date · Is the closest

2

Category values

Match or keep apart specific values, like one department with another.

  • Match with
  • Don’t match with

Sales · Match with · Engineering

3

Category overlap

Compare two lists that share the same options, like mentor skills and mentee goals.

  • Maximize overlap
  • Minimize overlap

Mentor skills ↔ mentee goals

4

Audience

Match or keep apart whole audiences, to connect two cohorts or two entities.

  • Match with
  • Don’t match with

New hires · Match with · Buddies

5

Specific members

Handle a named exception without touching the rest of the program.

  • Match with
  • Don’t match with

Nina Patel · Don’t match with · Marcus Lee

Always on

  • Meeting history comes first: people who already met, in any program, meet someone new whenever possible
  • Members are matched according to their selected preferences
Review

Check every group before anyone gets an intro.

See how well each group meets your rules, spot the pairs that already met, and fix them in a click. Send the round when it looks right to you.

  • With admin review, every round is generated as a draft to preview before sending
  • A match score on every group, with the rules it meets
  • A warning when two people were matched before
  • Generate new matches, shuffle, or upload your own
Matches to review Round 12 · sending Monday 9 AM
Approve and send
MT Maya Thomas Engineering · SF AM Alice Martin Marketing · SF
100% match
BK Bob Kim Marketing · NYC CL Claire Loeuvre Product · Chicago HB Hannah Brooks Product · NYC
67% match 100% match Previously matched with Claire Loeuvre Fixed: new pair
Matching rules quality Department · Is different Office · Is the closest Seniority · Is the furthest
PS Priya Shah Engineering · Chicago LR Lucia Romero Marketing · Chicago
100% match
Formats

Pairs, groups, mentors or a lucky draw.

Pick the matching mode that fits the moment. Your rules and meeting history apply to all of them.

Randomly

Coffee chats

Pairs or groups that change every round, guided by your rules.

Cross-groups

Connecting two entities

Every group brings together members of several audiences, like headquarters and field teams.

Group composer

Mentoring and buddies

Define every seat: one mentor and one mentee, one buddy and one new hire. A mentor can take several mentees per round, up to a maximum you set or they choose.

Lottery

Coffee with leadership

Draw one group from the participants, add fixed people like an executive, and schedule a new draw every 1 to 4 weeks.

Mystery 1:1s

Challenges and festive moments

Everyone is matched with someone without an intro, and you decide when to reveal the pairs.

2 to 10 people per group
4 options for incomplete groups, with a simulator: nobody is left alone
Every round starts from your company-wide meeting history

Loved by human & modern HR teams

Hear from some of our amazing customers who are building faster.

Reece Portwood
Reece Portwood
@Cox Enterprises
"One of my highlights of the year: I joined RandomCoffee Chats, a program designed to encourage networking and casual conversations with coworkers from different areas of the business."
Antoine Chapin
Antoine Chapin
@Crédit Agricole
"What's nice about RandomCoffee is that when I run into people who've taken part, they thank me for setting it up :)"
Savannah Charles
Savannah Charles
@Informa
"With the intention of connecting our global colleague base and learning more about each other's culture, we created a program with RandomCoffee called Cross-Cultural Conversations."
Felipe Gonzales
Felipe Gonzales
@Trimble
"I've had the opportunity to connect with colleagues across five different continents thanks to RandomCoffee!"
Michael Shea
Michael Shea
@Finalsite
"Couldn't be happier with RandomCoffee. What you guys are providing is pivotal to any semblance of company culture in a remote environment like ours."
Jo Shen
Jo Shen
@Electronic Arts
"It's a brilliant tool for us to connect and engage employees for people from different functions and locations to get to know each other."
Andreea Corendea
Andreea Corendea
@Flocert
"We love the virtual coffees dates and a lot of colleagues use it for informal exchange. This is exactly what we needed to foster our company culture."
Sarah Hatfield
Sarah Hatfield
@DisabilityIn
"Such a great way to build connections in a remote environment and our employees are raving about this! I cannot recommend your product enough!"
Isabel Shaw
Isabel Shaw
@HgCapital
"RandomCoffee team working has been excellent. They are on the ball, easy to contact, and have made the initial setup phase easy. Thanks, RandomCoffee!"
Hannah Wilson Baffoni
Hannah Wilson Baffoni
@Freshworks
"RandomCoffee has brought us to learn about different careers and life lessons in our industry."

Common questions about matching

How does the RandomCoffee matching algorithm work?

You rank your matching rules by priority. For every round, the algorithm scores the possible pairs or groups against those rules, the top rule always outweighing the ones below it, and keeps the combination with the best overall score for the whole program.

Which criteria can I match on?

Any profile field: department, office, seniority, language, skills, goals, or answers to a registration form. Rules can bring people together or keep them apart, and you can also match audiences or specific members.

Will people meet the same person again?

Meeting history comes first. The algorithm checks who already met across all your programs and pairs people with someone new whenever possible. In small groups, where everyone has already met, the oldest past partners become eligible again.

Can I review the matches before they are sent?

Yes. With admin review, each round is generated as a draft: you see every group with its match score and the rules it meets, get a warning when two people were matched before, and can regenerate, shuffle or edit groups before anything is sent.

Does it work for mentoring and onboarding buddies?

Yes. The group composer defines every seat, such as one mentor and one mentee, and a category overlap rule matches mentor skills with mentee goals. A mentor can take several mentees.

What happens with an odd number of participants?

Groups can hold 2 to 10 people. You choose how incomplete groups are handled, and simulate the split for any number of participants: nobody is left alone.

Start connecting today, for free.

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