A more measurable way to understand cannabis experience.
The original CANABIT material describes a platform that combines product details, user feedback, voice reflections, and real-world experience data. This page keeps the framing educational and insight-focused, not medical advice.
CANABIT is designed to help users and researchers compare reported outcomes with product chemistry and personal context. Over time, the project can support better product understanding, anonymized trend analysis, and more transparent community data loops.
Product upload and scan
Adds product information through barcode capture, OCR, or structured user input.
Voice experience recording
Lets users record reflections that can be organized into physical and emotional experience signals.
Dynamic ranking engine
Supports community-informed product scoring around specific goals and reported effects.
Business dashboards
Gives dispensaries and producers anonymized trend insight without exposing individual user records.
Researcher onboarding
Creates a path for academic and commercial researchers to collaborate with consent-driven community data.
Future matching layers
The source material points toward terpene, biometric, and pathway-based matching as future research directions.
Cannabis data is noisy. CANABIT adds signal.
Strain names, THC percentages, and product descriptions rarely explain how people actually feel. CANABIT gives AxonDAO a privacy-first lane for turning real-world cannabis experiences into structured, comparable insight.