GemmaCert
Cannabis Quality Detection and Analysis
Company Overview
Snapshot
Founded in June 2015 by Guy Setton and Oded Shoseyov, GemmaCert operates with 11–50 employees. The company has raised a total of $6.75 million across 3 funding rounds from 7 investors. In April 2024, GemmaCert ceased operations.
Business overview
GemmaCert develops a non-destructive and non-invasive analytical device for the precise verification of cannabis flower potency. Its proprietary technology combines advanced optics and imaging analysis to provide real-time results without requiring sample preparation, solvents, or grinding. The device is designed for growers, processors, distributors, and dispensaries, offering increased quality assurance and operational efficiency. GemmaCert operates in the Health Tech & Life Sciences sector, specifically within Pharma & Medical Biotechnology and Medical Cannabis, and also serves the Agriculture & Food Technologies market.
Strategic signal
In April 2024, GemmaCert ceased operations, indicating a significant shift in its business trajectory. This development suggests that the company is no longer actively pursuing its mission of providing cannabis quality detection and analysis, which could impact stakeholders in the medical cannabis and agritech sectors who relied on its technology.
Company Intelligence Q&A
- What is GemmaCert's primary focus?
- GemmaCert focuses on developing a non-destructive and non-invasive analytical device for the precise verification of cannabis flower potency, serving growers, processors, distributors, and dispensaries.
- When was GemmaCert founded?
- GemmaCert was founded in June 2015.
- What is the current operational status of GemmaCert?
- GemmaCert ceased to operate in April 2024.
- Which investors participated in GemmaCert's February 2017 Seed round?
- In February 2017, GemmaCert received Seed funding from Agrinnovation and Mayham Holding AG.
- What technology does GemmaCert utilize for its cannabis analysis device?
- GemmaCert's proprietary technology combines advanced optics and imaging analysis, along with machine learning, to overcome challenges in analyzing heterogeneous cannabis flowers.