01
Student submits a request
The brief captures subject, deadline, context, and what kind of help the student needs.
About MathGoose
We are the trust layer between STEM students and the right tutor.
MathGoose combines AI triage, tutor matching, quality review, payment protection, and privacy cleanup so students can get help without guessing from a random directory.
What we do
Matching itself is a real service.
Students bring messy requests: a proof they do not understand, a coding bug, a statistics assignment, an exam topic list, or a concept that needs a patient explanation. A good tutor match depends on the exact context, not just the word “math” or “CS.”
MathGoose reads the request, builds a structured learning brief, and routes it to tutors whose background, course strengths, activity level, availability, and past feedback fit the job.
The middle layer matters
We help both sides trust the handoff: students get clearer tutor choices, and tutors receive cleaner context before accepting work.
80%-90%
goes to the tutor for accepted tutoring work
Tutor share
10%-20% platform fee
Matching
Verification
Quality checks
Privacy cleanup
How we charge
Most of the payment goes to the tutor.
MathGoose keeps a low platform fee, usually between 10% and 20%. That means at least 80% to 90% of the student payment goes to the tutor when the tutoring work is accepted.
The platform fee supports the work students do not see: tutor verification, matching infrastructure, AI-assisted quality checks, privacy redaction, dispute handling, and the product systems that make the handoff safer.
Database plus algorithm
We can see signals students cannot see alone.
Finding a strong tutor online can feel like searching a huge ocean with almost no map. MathGoose builds that map from platform data: recent activity, completed sessions, response patterns, topic strengths, uploaded course coverage, price range, and student feedback.
When a new request arrives, those tutor signals are compared with the actual assignment brief. That lets us rank matches by fit instead of asking the student to manually inspect every profile.
Tutor signal timeline
Private platform data
Calculus + Python tutor
Active this week
This week
2 sessions
Last week
3 sessions
This month
13 sessions
Calculus II
Linear Algebra
Python
Regression
Request fit
94%
Quality and payment protection
Payment should not depend on blind trust.
MathGoose adds an acceptance checkpoint between the student and the tutor. We use the student brief, tutor submission, AI-assisted review, and human judgment when needed to check whether the work is useful, complete, and aligned with the tutoring request.
01
02
Tutor accepts the clean brief
The matched tutor sees enough context to decide whether they can help well.
03
Quality is reviewed
AI-supported checks help detect incomplete, off-topic, or low-quality work before release.
04
Payment is released or held
If the result passes review, the tutor is paid. If not, MathGoose can request revision, rematch, or protect the student payment.
→
School removed
Professor hidden
Privacy before handoff
Students can remove sensitive information before a tutor sees it.
A request can contain school names, professor names, course codes, student IDs, emails, metadata, or screenshots with personal details. Some students want help without exposing where they study or who teaches the course.
MathGoose can automatically detect and redact sensitive details, show the student a preview, and only send the cleaned version to the matched tutor after student approval.
Upload
AI redaction
Student review
Clean tutor brief
A safer handoff
Get help now
Start with a question. Let MathGoose handle the matching layer.
AI first, human tutor when needed, with privacy and quality checks around the handoff.