QR Tags for Universities — Smart ID for Student Belongings
Universities deal with lost laptops, books, lab equipment daily. Nishaaan QR tags for LUMS, NUST, UET, FAST, and COMSATS students — bulk partnerships for smart campus lost & found.
The University Lost Item Crisis
Pakistan's top universities — LUMS, NUST, UET Lahore, FAST NUCES, COMSATS, IBA Karachi — handle thousands of lost item reports every academic year. Laptops left in labs, calculators forgotten in lecture halls, bags left in cafeteria booths, library books taken to the wrong hostel room. The current system (a notice board or a physical lost-and-found box) is wholly inadequate for campuses with 5,000-15,000 students.
Most Commonly Lost Items on Campus
- Laptops — especially in computer labs and co-working spaces
- Scientific calculators — left in lecture halls after exams
- Lab equipment — microscopes, instruments, measurement tools
- Bags and backpacks — left at cafeteria tables or in common areas
- Chargers and earbuds — the most frequently lost small items
- Books and notebooks — especially during exams when students carry many
- Sports equipment — football boots, cricket gear, tennis rackets
- Prayer mats and accessories in university masjids
The Nishaaan University Partnership Model
- 1University partners with Nishaaan and purchases bulk tags at semester start
- 2Tags are distributed to students — each is unique and registered to one student
- 3Students attach tags to laptops, bags, calculators, sports gear
- 4When a teacher, guard, or fellow student finds a lost item, they scan the QR
- 5Owner receives notification instantly and collects their item from the finder
- 6University admin can view aggregate lost-and-found analytics through the admin dashboard
Per-Student Pricing
Bulk university pricing starts at significant discounts for 100+ students.
Zero App Needed
Finders don't need to download anything — just scan and contact.
Student Privacy
Student phone numbers are never shown — anonymous contact bridge.
Admin Dashboard
University admin gets aggregate data — which buildings have most lost items, peak times, etc.
🏛️ University partnership