Mentor & Tutor (Learning Companion)
A personalised AI learning companion that supports each student's progression, concept-by-concept tutoring grounded in your syllabus, with measurable mastery tracking and graceful handoffs to faculty mentors when the moment calls for one.
One faculty mentor for two hundred students is the unspoken reality across most Indian universities. Mentors do their best, but the nightly "I'm stuck on this concept" loop never reaches them, so students fall behind silently and surface only at exam time, when the gap is too wide to close.
The Learning Companion lives inside the student's day. It knows the active syllabus, watches exam performance, and shows up with a five-minute refresher exactly when the student needs it. Faculty mentors stay in the loop on the things that actually need a human.
What do you want to learn today, Aarav?
How it works
Knows your syllabus
Companion is grounded in the active syllabus, course textbooks, past lectures, and the student's own performance history, not the open internet.
Spots weak concepts
Bayesian Knowledge Tracing keeps a per-concept mastery estimate that updates with every quiz, exam, and tutoring exchange.
Runs short drills
Five-minute refreshers, two-problem warm-ups, code walkthroughs in plain language, paced to the student, not the cohort.
Loops in faculty
Stuck signals, sentiment shifts, and repeated failed drills escalate to the faculty mentor with full conversational context.
A day in the companion's life
Personalised refreshers, syllabus-grounded code walkthroughs, daily goals, all running quietly in the background while the student is studying.
semaphore parking_spots = 5; P(parking_spots); // a car arrives // … parks … V(parking_spots); // a car leaves
Built to work alongside faculty, not replace them
Syllabus-grounded
Pulls from the active syllabus, course textbooks, and past lectures. Refuses to drift into open-internet hallucinations.
Per-concept mastery
Bayesian Knowledge Tracing tracks every concept individually, so the companion knows what to refresh and what to leave alone.
Faculty-in-the-loop
Stuck signals, sentiment shifts, and repeated failed drills escalate to the mentor with full conversational context.
Daily goals & streaks
Personalised pacing keeps engagement high without homework spam. Streaks reward consistency, not time-on-screen.
Placement-prep modules
Beyond core CSE, aptitude, logical reasoning, verbal, application English, spoken English. Each in the format the real assessment uses.
LMS-native
Reads syllabus from your LMS, performance from your SIS gradebook. Mastery and escalations push back as structured artefacts.
Frequently Asked Questions
Generic chatbots answer any question with confident-sounding text. The Learning Companion is grounded in your syllabus, your textbook, your past lectures, and the student's own performance history. It refuses to drift outside the curriculum and surfaces exactly the concept the student got wrong on last week's exam, not a hallucinated approximation.
No. It bridges the mentor-to-student ratio gap. A faculty mentor in an Indian university typically owns 80 to 200 students. The companion handles the nightly "stuck on a concept" loop so that face-to-face mentor time goes to the harder conversations, career direction, research projects, motivational support. The companion escalates back to the mentor on stuck signals, sentiment shifts, or repeated failed drills.
It draws from three signals, the active syllabus and where the student is in it, exam and quiz performance with criteria-level breakdowns, and prior tutoring conversations. Bayesian Knowledge Tracing keeps a per-concept mastery estimate that updates with every interaction.
They can ask anything, but the companion is tuned to redirect off-syllabus questions back to the curriculum or, for placement-prep domains (aptitude, logical reasoning, verbal, English), to the placement-prep modules. It will not answer "do my homework" prompts; it will scaffold the student to the answer.
A weekly digest per cohort, students at risk, concepts the cohort is collectively struggling with, escalations from the companion that need human follow-up, and individual student trajectories. Faculty can also dip into any student's tutoring history with consent.
SSO with your IDP, syllabus pulled from the LMS, performance signals pulled from your SIS gradebook and any QverLabs Skills Assessment or Exam Evaluation deployments. Companion outputs (concept mastery, escalations) push back to the LMS as structured artefacts faculty can act on.
Close the mentor-to-student gap.
See the companion in action and walk through how it integrates with your syllabus and faculty workflow.
Schedule a call