Demo flow

See how field + language mentorship moves someone from stuck to supported.

This page explains the wedge: a student chooses their field and language, requests a verified sensei, schedules a session, prepares with AI, and records outcomes without needing a login.

Best first beachhead

Start narrow: 5–15 students and 5–10 verified senseis in one community, language, or field. For example, Telugu-speaking engineering and IT professionals in Alberta.

Students onboarded
Senseis approved
Sessions completed
Resumes/interviews/referrals tracked
Confidence before/after measured
01

Student joins with field + language context

A skilled newcomer, international student, or early-career professional shares their target field, current barrier, preferred language, and what kind of mentor would help.

02

Sensei applies and gets reviewed

A mentor applies with professional background, LinkedIn/work details, boundaries, and availability. Admin review happens before public visibility.

03

Student requests mentorship

Students do not randomly message everyone. They request a sensei, and the sensei or admin accepts before the conversation opens.

04

AI helps prepare for the session

Life Dojo AI helps the student create better questions, a short self-introduction, and a session agenda — without replacing the human mentor.

05

They schedule and meet

The session can use Google Meet, Calendly, Zoom, Jitsi, or another link. Both people receive email and calendar details.

06

Progress becomes measurable

After the session, students reflect and record outcomes like resume update, mock interview, referral, interview, job offer, or confidence change.

What success looks like

The first target is not thousands of users. The first target is five real mentorship sessions completed, clear student feedback, and proof that verified mentors plus structured session prep can create career clarity.