Smart matching. Not random pairing.
Twelve signals. Zero self-reported data. The algorithm that finds the peer you actually need — not just the one who used the right keyword.
Five primary matching dimensions.
Plus seven secondary signals. Twelve total. Zero self-reported data points.
Skill complementarity
You're matched with someone 1–2 tiers above your current level in your focus skill — close enough to be relevant, far enough to be valuable.
Goal alignment
Your stated focus skill is matched to their teaching history. Not their profile headline — their actual completed teaching sessions.
SCI level
SCI tier gap is optimized for learning productivity. Sessions between two Newcomers rarely produce strong evidence for either party.
Availability
Real-time availability from your calendar — not self-reported. Only practitioners with genuine scheduling overlap are surfaced.
Session history
Past session completion rates and ratings from both parties. Practitioners with low reliability scores are deprioritized to protect your time.
How a match is made.
From intent to session in minutes.
You signal your intent
Set your focus skill. The engine pre-ranks practitioners by composite match score — not by recency or profile completeness.
Twelve signals run in real time
Skill tier gap, session history, calendar availability, language, pace, reliability — all computed simultaneously. The best match may not be the most active profile.
You review and request
See your top matches with the primary matching dimensions surfaced. Request a session. Most practitioners respond within two hours.
Session generates evidence
The completed session becomes an immutable evidence event for both parties. Your SCI updates within 60 seconds.
Questions about matching.
No. Every signal in the matching engine is derived from observable behavior: sessions completed, ratings received, assessments passed, calendar availability. We never ask you to describe your own learning style or communication preference — we infer it from what you've done.
Yes. You can filter by skill, tier range, language, and availability. The engine's default ranking is by composite match score, but you can override any dimension.
The next highest-scoring match is surfaced. Decline rates are tracked — practitioners who frequently decline see a temporary reduction in visibility while the system re-evaluates their availability signals.
If both parties can learn from each other — you know Python, they know TypeScript — the engine can surface exchange sessions where both parties teach and learn. These generate evidence for both simultaneously.
Find your next teacher in minutes.
The engine does the work. You show up.