Newton Insights has introduced its AI-powered Smart OS, a cutting-edge process-control platform tailored for the cannabis extraction sector. This system marks a significant departure from conventional methods, which often depend on spreadsheets and intuition, by providing operators with inline cannabinoid quantification, intelligent alerts, cost monitoring, and continuous optimization. The platform is designed to integrate seamlessly into existing workflows, eliminating the need for disruptive changes.
Kellan Finney, Co-Founder of Newton Insights, emphasizes the transformative potential of the Smart OS, highlighting its ability to deliver real-time data on environmental conditions and extraction efficiency. Initially focused on hydrocarbon extraction, with plans to expand to ethanol workflows, the platform leverages live sensor feeds and AI-driven alerts to facilitate science-backed adjustments during operations.
The benefits of the Smart OS have been evident during its pilot phase, with industry partners reporting reduced idle time and the capacity for an additional extraction run per day. One early adopter has seen daily revenue increase by over $4,000, underscoring the platform's potential for significant annual financial gains.
Central to the Smart OS are precision optical sensors and a cannabis-trained LLM, which enable real-time cannabinoid quantification, solvent ratio tracking, and live data benchmarking. Anticipated updates in Q4 aim to broaden the platform's capabilities to include visibility into chillers and other IoT devices, further boosting operational efficiency.
For those seeking detailed information on the Smart OS, Newton Insights provides a technical whitepaper available for download at https://www.newton-insights.com, offering comprehensive insights into the platform's features and advantages.
The launch of the Smart OS by Newton Insights represents a pivotal advancement for the cannabis extraction industry, equipping operators with the means to transition from reactive guesswork to proactive, science-based control.
