The Case of the Counterfeit Cure

A client came to me with a quiet but dangerous problem: counterfeit medicine was circulating in packaging convincing enough to fool the eye, and the regions hit hardest had no fast, affordable way to check what they'd bought.

The brief was blunt. Build something that can look at a box of medicine the way a trained inspector would, say plainly whether it's real, and do it cheaply enough to actually reach underserved regions — not just a proof of concept for a lab.

I built a Python web application that pairs OCR with LLaMA-3, run through Groq for speed, to deliver rapid, low-cost authenticity verification straight from a photograph of the packaging.

Because low cost only matters if people can actually use the tool, I integrated a voice-enabled conversational AI chatbot on top of the verification pipeline — giving users personalized, real-time support instead of a bare pass/fail result.

The two pieces together — fast OCR-to-LLM verification and a chatbot that talks the user through it — were built specifically to streamline counterfeit detection in places where neither lab equipment nor literacy can be assumed.

A working application that turns a phone photo into a fast, low-cost counterfeit-medicine verdict — with a voice-enabled chatbot alongside it so the result actually reaches the people who need it.