Cannabis Retail Recommendation System
Overview
The Challenge
A cannabis retail platform aimed to solve a common problem in the industry: helping customers confidently choose products that align with their desired mood or outcome. Relying on strain names or THC percentages was proving unreliable and confusing. We developed a web platform that uses terpene and molecular data from regulatory lab tests, combined with AI-powered algorithms, to deliver tailored recommendations based on the user's intent, such as relaxation, focus, or sleep. Users can also create personal profiles that track their past experiences, enabling smarter suggestions over time. Integrated with retail dispensaries, the platform enhances in-store interactions by enabling retail staff to offer data-backed guidance. The system is now helping reduce product guesswork and boosting customer satisfaction at the point of purchase.
Our Approach
How We Did It
Researched
Mapped user needs and identified scientific data sources.
Engineered
Built AI-driven matching logic and responsive web platform.
Integrated
Connected to dispensary systems and launched pilot beta.
Impact
The Results
The platform launched in beta with live dispensary integrations, bringing structured, data-backed recommendations to retail cannabis. Early feedback highlighted improved customer confidence, reduced staff burden, and a smoother buying experience.
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