AI Matching
Transparent mentor matching, built for real constraints
ShamMentor uses AI-assisted ranking in the backend to match members with mentors by goal fit, domain relevance, language, availability, and trust signals. Every recommendation is explainable and feedback-ready.
How the matching pipeline works
Current backend flow: candidate retrieval, ranking, explanations, then user feedback to improve future quality.
quick_match
Fast recommendations when members need immediate direction with top-fit mentors.
goal_match
Higher emphasis on goal alignment and expected outcomes for the specific request.
mentor_type_match
Matches by mentor type and practical background suited for the member journey.
What affects your match quality
The engine does not rely on one signal. It combines profile fit, language/context, activity and trust indicators.
Domain and practical expertise fit
Language and local context alignment
Availability and timezone compatibility
Trust signals: reviews, verification, behavior
Interaction feedback (profile open, like, booking intent)
Variant rollout and admin-tunable controls
Trust, safety, and governance around matching
Matching quality is monitored alongside moderation and reporting workflows so platform trust is not separated from recommendation quality.
Reviewable explanations
Backend responses include score factors and reason codes to reduce black-box behavior.
In-app feedback loop
Members submit like/dislike and intent events that improve ranking quality over time.
Admin governance + safety alignment
Admin overview and weights tuning are aligned with reports, reviews, and moderation operations.
Test AI Matching with your use case
Join as a mentor or register as a member to test matching quality on real goals and constraints.
Submit interest and we will respond quickly
The lead form supports member and mentor demand capture with validation and anti-spam protection.