Building a 3-second medicine verifier with Gemini vision + pg_trgm fuzzy matching

The problem

Indian households spend roughly ₹65,000 crore every year on out-of-pocket medicine costs. A meaningful slice of that is going toward branded drugs when the exact same molecule — same active ingredient, same dosage, same CDSCO approval — is sitting in a Jan Aushadhi store for a fraction of the price. Dolo-650 retails at ₹32 a strip; the Jan Aushadhi paracetamol is ₹4.90. The cheaper medicine isn't the issue. Nobody tells the patient it's there.

We built

Stack

• 
  Frontend: React 18 + Vite, client-side image compression before upload (cuts 4MB phone photos to ~250KB JPEG)


• 
  Vision + NLG: Gemini 1.5 Flash (one call for extraction, one for the plain-English explanation)


• 
  DB: Supabase / PostgreSQL 15 with pg_trgm
  


• 
  API layer: Vercel serverless functions, ESM (type: module)


• 
  i18n: 6 Indian languages (EN, HI, TA, BN, TE, MR)


• 
  Hosting: Vercel

The price engine falls back to a hardcoded JAN_AUSHADHI_DB if Supabase can't be reached — every result still has a price row.

What's next

A WhatsApp bot. 500M+ Indians use WhatsApp; nobody wants to open a browser at the chemist counter. A photo-to-number flow would 10x reach overnight. Then offline mode for Tier-3 Jan Aushadhi stores with poor connectivity, and a "find the nearest Kendra" map to close the last-mile gap.

Try it:

Team: Aman Sachan, Siddharth Lalwani, Chetna Kalra, Syed Akbar — Team Agada, Open Innovation 2026.

Built with Gemini vision + a government drug database to fight pharma price gouging. No data retention, no paywalls, no accounts.