Matching that shows its work.

Most matching tools give you a score. Pairwise gives you the reasoning.

Explore PairsUp to Date
Frederique B. → Keshaun H.88
Match Rationale

Strong alignment on data science goals and collaborative communication style.

Alignment Points
Shared technical focus
Growth mindset aligned

What makes a great match in your program?

Pairwise comes with research-backed matching strategies, but you choose which ones run, how heavily they're weighted, and what thresholds matter for your program. The application form follows from the flow you design; your participants answer only the questions Pairwise needs to evaluate matches the way you've configured them. Programs that prioritize capacity over goal alignment can dial it that way. Programs that filter hard on industry can do that too.

Assessment FlowDraft
START
Availability
3 questions
Individual
Engagement Readiness
6 questions
Individual
Eligible for Matching?
Checkpoint
Yes
No
Core Compatibility
7 questions
Matching
Goal - Expertise Alignment
5 questions
Matching
Professional Context
4 questions
Matching
Application Rejected
Outcome

Scoring Weights

Tune per program
Core Compatibility50%
Goal - Expertise Alignment30%
Engagement Readiness20%

Why this match, not that one?

Once you've set the rules, Pairwise shows its work. When you make a match, the case for it is already written: rationale, alignment points, areas to consider, conversation starters, and how this candidate compared to others. So when a mentor asks "why me?" or your director asks "why them?", you've got the answer.

Frederique B. → Keshaun H.

Rank #1

87.97

Match Rationale

Keshaun's focused data science goals align directly with Frederique's expertise in ML and Data Analysis.

Key Alignment Points

Data science skill alignment
Career development focus
Collaborative communication style

Areas to Consider

May need more structured guidance than the mentor typically provides.

Conversation Starters

What drew you to pursuing a career in data science?

Browse, compare, decide.

When your cohort has 50 mentors and 200 mentees, Explore Pairs is what makes finalization manageable: ranked recommendations per mentor, the rationale always visible, and side-by-side comparison when you need to decide between similarly-strong candidates. You're making decisions, not scrolling through spreadsheets.

Compare candidate: Keshaun Hamill

Compared with Marcus Chen, Priya Shah

MentorFrederique Bartell
Fit score87.97
Rank#1
Key alignment
  • Data science skill alignment
  • Collaborative communication
Comparison NotesStrongest expertise match; growth-aligned
Action

How it fits with the rest of Pairwise

Matching is one moment in a longer arc. Pairwise also handles the automated emails that bring participants into the program, the scheduling and prep that keeps meetings happening, and the engagement signals that tell you the program is on track.

See it for yourself.

Demos are 30 minutes. I'll show you the platform, hear about your program, and tell you honestly whether we're a fit.