Cohort & batch management
Batches, semesters, electives, faculty allocation and attendance modelled as first-class objects — not bolted onto a course list.
Products — Learning infrastructure
The learning system institutions can actually run.
A learning platform built for Indian engineering and degree colleges — cohort management, project-based tracks, continuous assessment, an AI mentor for students, and the placement-readiness analytics your training and placement cell has been assembling by hand.
0Partner institutions
0+Students trained
01 — The problem
Why most campus LMS deployments fail
A college buys an LMS, uploads PDFs to it, and within two semesters it has become a file server that students log into twice a year. The platform was designed for corporate compliance training — a linear course, a completion tick, a certificate — and it does not model anything a college actually needs: batches, semesters, electives, faculty allocation, internal marks, or placement readiness.
Meanwhile the placement cell keeps the information that matters in a spreadsheet. Who has finished which training. Who can write code without help. Who is ready to be put in front of a recruiter next month. None of it is in the LMS, because the LMS was never asked to answer that question.
So the institution ends up paying for a system that stores content while running its real decisions on a shared drive.
02 — The solution
What we built instead
LARE LMS treats the cohort, not the course, as the primary object. A batch has a timetable, an assigned faculty member, a stream of assessments and a readiness score that updates as students work — which means the platform can answer the placement cell's question directly.
Learning is project-based. A track is a sequence of problems, each producing an artefact that is reviewed, graded against a rubric and retained in the student's portfolio. Video and reading material support the work rather than constituting it.
And because assessment runs continuously rather than at semester end, both the student and the department can see a gap while there is still time to close it.
03 — Capabilities
What's in it.
9 capabilities, each of which exists because something specific went wrong without it.
Project-based tracks
Sequenced problems producing reviewable artefacts. Rubric-based grading with written reviewer feedback on every submission.
AI mentor
A grounded assistant that answers from the student's own track content and submission history. It explains and points to the material — it does not write the submission.
Continuous assessment
Quizzes, coding tasks, vivas and project reviews rolled into one moving score, visible to student, faculty and department.
Placement readiness scoring
A per-student composite across technical work, assessment history, communication and consistency — the number the placement cell actually needs.
Verifiable certificates
Credentials tied to demonstrated work, with a public verification page listing the artefacts behind them. Not a PDF anyone can forge.
Gamification that isn't noise
Streaks, cohort leaderboards and milestone badges, scoped so they encourage consistency rather than grinding.
Analytics & reporting
Department, batch and student-level reporting, exportable for NAAC, NBA and internal academic audit.
Integrations
SSO via Google Workspace and Microsoft Entra, ERP sync for student records, and a REST API with webhooks.
04 — Dashboards
Three audiences, three views.
Each role sees the question it actually needs answered — not the same dashboard with rows hidden.
Student dashboard
One screen answering: what am I meant to be doing today, how am I actually doing, and what is standing between me and being placed.
- Active track with next deliverable and deadline
- Submission history and reviewer feedback
- Readiness score with a breakdown of what's dragging it down
- AI mentor grounded in the student's own material
- Portfolio of reviewed artefacts, exportable and shareable
- Certificates with public verification links
Faculty dashboard
Batch oversight and the review queue, without hunting through folders.
- Batch roster with attendance and momentum flags
- Submission review queue with inline rubric grading
- Students falling behind, surfaced before they disappear
- Content scheduling against the academic calendar
- Bulk feedback and cohort announcements
Institution dashboard
The department-level view, and the reports that would otherwise be assembled by hand each accreditation cycle.
- Cross-batch and cross-department comparison
- Placement-readiness distribution by branch and year
- Faculty delivery and review-turnaround metrics
- NAAC / NBA export packs
- Programme outcome and attainment mapping
05 — How it runs
End to end.
The same sequence every time, which is what makes the reporting a query rather than an archaeology project.
- 01Institution
Cohort set up
Batches imported from your ERP or uploaded as a sheet. Faculty assigned, calendar mapped, track selected.
- 02Student
Baseline assessment
A short diagnostic places each student on the right rung so the cohort doesn't move at one pace for everyone.
- 03Student
Project work
Sequenced problems, each producing an artefact. Reviewed against a rubric with written feedback, not just a score.
- 04Faculty
Review & intervene
Faculty grade submissions and act on the flags for students losing momentum, while there is still time.
- 05Student
Readiness score rises
The composite score moves as work lands, and shows the student exactly which component is holding it back.
- 06Institution
Placement handoff
The T&P cell filters by readiness, exports a recruiter-facing shortlist, and pushes it straight into LARE Drive.
06 — Why this one
What makes it different.
Built for the Indian academic calendar
Semesters, internal marks, electives, lab batches and attendance regulations are modelled in the data layer — not worked around with custom fields.
Readiness, not completion
Completion percentage tells an institution nothing useful. Readiness scoring is the feature the platform is organised around.
The AI mentor refuses to do the work
It is grounded in the student's own track material and explicitly declines to produce submittable answers. An assistant that does the assignment destroys the signal the platform exists to generate.
Runs on unreliable connectivity
Content caches locally and submissions queue offline. Built and tested against campus networks in tier-two and tier-three cities.
07 — In production
Where it stands.
0Partner institutions
0+Students trained
0%Programme completion
0%UptimeTrailing 12 months
08 — Pricing
What it costs.
Published figures are pending finalisation — request pricing and you'll get a real number against your actual cohort size, not a range.
Department
A single department or branch running one or two tracks.
₹—per student / year
- Up to 300 students
- Student, faculty & HoD dashboards
- Project-based tracks
- Continuous assessment
- Verifiable certificates
- Email support
Institution
Most commonCampus-wide deployment with placement integration.
₹—per student / year
- Unlimited students
- Everything in Department
- Placement readiness scoring
- LARE Drive integration
- NAAC / NBA export packs
- SSO & ERP sync
- Named success manager
Consortium
University systems, groups and government skill missions.
Custommulti-campus
- Multi-campus tenancy
- Everything in Institution
- Cross-campus benchmarking
- White-labelling
- Custom integrations & API access
- On-site onboarding
- Programme-level reporting
See LARE LMS on your own data.
Thirty minutes in a working environment with your scenario loaded. If it's a bad fit we'll tell you in the call rather than three emails later.