Product demo
Don't watch a demo. Run a college day.
Anugat AI on a sample engineering college. One faculty absence at 08:31, five modules, one Tuesday. Every step is yours to take.
- About 3 minutes
- No sign-up
- Sample data
Short on time? See the whole day in 0:24.
One Tuesday with Anugat AI. A faculty absence at 08:31 is covered, taught, heard from and filed as NAAC evidence by 16:05, with nothing typed twice.
Your day · HOD, CSE
31 classes today. One absence to cover before 09:50.
Plans are drafted overnight from each course plan. Nothing reaches students until faculty approve it.
Department meeting · mid-semester coverageDr. M. Iyer · Prof. S. Khan · Prof. K. Patil · Dr. V. Nair · Dr. N. Joshi
Agenda · carried forward
- Open actions from 1 Sep, two still pending
- Unit 3 coverage across the Sem 5 core papers
- Internal assessment calendar, October
| Faculty | Plans approved | Response rate | Hours saved |
|---|---|---|---|
| MIDr. Meera IyerDBMS | 18 / 20 | 74% | 3.5 h |
| SKProf. Salma KhanOperating Systems | 16 / 20 | 68% | 2.5 h |
| KPProf. Kiran PatilAlgorithms | 19 / 20 | 81% | 4.0 h |
| VNDr. Vikram NairComputer Networks | 12 / 20 | 48% | 2.0 h |
| NJDr. Neha JoshiSoftware Engineering | 9 / 20 | 46% | 1.5 h |
2 courses sit below a 50% response rate. Low rates usually mean the sample rotation is stale, not that students disengaged. Worth a look before reading anything into the scores.
- 1Cover the absence
- 2Approve the class pack
- 3Hear from the class
- 4Close the outcome gap
- 5Export the evidence
Step 1 of 5. Dr. Iyer can't take Tuesday P2. Open the Timetable, re-plan the day and approve a substitute.
Press 1–6 to switch views. Everything runs in your browser; nothing is sent anywhere.
What you just ran
One absence. Five modules. Nothing typed twice.
Every module reads the same record. That's why the substitute knew 2NF hadn't landed, why Thursday's class opens with the right example, and why the evidence was already filed before anyone asked for it.
- 01
Timetable
Absence covered
Waiting on you
—
- 02
Preparation
Class pack approved
Waiting on you
—
- 03
Feedback
Class heard
Waiting on you
—
- 04
Curriculum
Outcomes updated
Waiting on you
—
- 05
Accreditation
Evidence filed
Waiting on you
—
Vision & Mission
Institutions shouldn't have to choose between teaching well and proving it. With Anugat AI, the same work does both.
Every institution in India can prove learning, not just report it.
Turn daily academic operations into better learning outcomes and accreditation backed by proof.
Under the surface
The details that decide whether it works on a Monday
- The solver shows its working
- Hard rules such as clashes, weekly load and subject competence are never traded off. Soft preferences are scored, and every recommendation says why, including the options it set aside.
- Every change has an owner
- Faculty raise it, the HOD or programme coordinator approves it, and the substitute gets the class context. Each step is logged with a time and a name.
- Feedback students don't resent
- Ten random students per class, with a rest period so no one is asked twice in a row. Three taps and an optional line, on the phone they already carry.
- Outcomes stay traceable
- Every unit maps to course and programme outcomes, aligned to NEP 2020. Understanding is measured after each class, not inferred from end-semester marks.
- Evidence in the assessor's format
- Records are filed against NAAC key indicators as the work happens, in Data Capture Format, so the IQAC reviews instead of chasing.
DBMS · Tue P2 · verified by university email
Answers are pooled. None is linked to your name.
Now run it on your own timetable.
In a live demo we load your sections, faculty and outcomes, and walk your HODs and IQAC through a real week.