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NEET Counselling Platform (mediCareers)

From Aspirations to Admissions — a data-driven NEET-UG counselling platform with a rank-based college predictor and personalised guidance packages.

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NEET Counselling Platform (mediCareers) home page on desktop
NEET Counselling Platform (mediCareers) home page on mobile

The short version

Full results ↓
  • Students get a clearer, easier college-prediction experience than the competing apps we benchmarked against during design
  • Students can choose the level of support that fits them — from a free self-service prediction to a paid, personalised counselling package
  • The mediCareers team can update colleges, counselling flows, packages, and pricing themselves at any time, without waiting on a developer

The challenge

What they were up against.

NEET-UG candidates in India face a confusing, high-stakes college-selection process — thousands of colleges, multiple quota types, and closing ranks that shift every year — where guessing wrong costs a student their year. Before building mediCareers, we looked closely at a number of existing NEET counselling apps and found the same recurring problems: clunky, confusing prediction tools, rigid one-size-fits-all guidance with no real personalisation, and content and pricing that were hardcoded, so the platform's own team couldn't update colleges, packages, or counselling flows without going back to a developer. Students needed more than a rank predictor — they needed a range of guidance options, from a quick self-service check to real 1-on-1 support, presented in a way that was actually easy to use.

What we built

How we solved it.

  1. 1

    Studied a range of existing NEET counselling platforms first, specifically to identify where their UX broke down for students, then designed mediCareers to fix those exact pain points

  2. 2

    Built a rank-based College Predictor that classifies every eligible college into Safe/Moderate/Reach bands using several years of real closing-rank history

  3. 3

    Built multiple guidance packages so students can choose anything from a quick self-service prediction to full personalised 1-on-1 counselling

  4. 4

    Made the entire platform dynamic — colleges, master data, counselling flows, packages, and pricing are all managed by the mediCareers team through an admin panel, with no code deployment needed for routine updates

  5. 5

    Built a fully database-driven counselling rule engine so new state or all-India-quota counselling flows can be added the same way

  6. 6

    Gave students self-serve registration with WhatsApp OTP verification and a clear path to upgrade into a paid guidance package

  7. 7

    Shipped as an installable PWA with SEO tooling so public pages are discoverable

  8. 8

    Deployed on a hardened production stack behind Nginx/TLS with an automated CI/CD pipeline

Screens

See it for yourself.

Home page1 / 4

Key features

What users get.

  • A rank-based college predictor built on years of real admission data, not guesswork

  • Multiple guidance packages, from self-service prediction to personalised 1-on-1 counselling

  • An entirely dynamic platform — counselling content, colleges, and pricing are all managed without a code deployment

  • A UX benchmarked against many existing counselling apps, then refined to fix their common pain points

  • Students verify themselves through WhatsApp OTP verification, no separate app needed

  • Installable as an app directly from the browser, no app store needed

The result

What changed.

  • Students get a clearer, easier college-prediction experience than the competing apps we benchmarked against during design
  • Students can choose the level of support that fits them — from a free self-service prediction to a paid, personalised counselling package
  • The mediCareers team can update colleges, counselling flows, packages, and pricing themselves at any time, without waiting on a developer
  • The platform is live in production at medicareers.in, and the site itself reports guiding over 15,000 students through NEET counselling

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