Newton Insights has introduced its AI-powered Smart OS, a process-control platform for the cannabis extraction industry that provides inline cannabinoid quantification, intelligent alerts, cost monitoring, and continuous optimization without requiring workflow changes. This system moves operators beyond traditional spreadsheet-based methods, offering real-time visibility into extraction operations.
The platform is initially designed for hydrocarbon extraction, with ethanol workflows to follow. It integrates live sensor feeds, inline cannabinoid quantification, and AI-driven alerts that allow for science-backed adjustments during runs. According to Kellan Finney, Co-Founder of Newton Insights, the Smart OS transforms how operators approach their work by delivering real-time data on environmental conditions and extraction efficiency.
During a pilot phase with industry partners, the platform demonstrated significant benefits, including reducing idle time to allow an additional extraction run per day. One early adopter reportedly increased daily revenue by over $4,000, highlighting the potential for substantial annual gains. At its core, the Smart OS uses precision optical sensors and a cannabis-trained large language model (LLM) for real-time quantification of cannabinoids, tracking of solvent ratios, and benchmarking of live data.
Future updates in Q4 aim to extend visibility to chillers and other IoT devices with closed-loop alerts, further enhancing operational efficiency. For those interested in learning more, Newton Insights offers a technical whitepaper for download at https://www.newton-insights.com, providing an in-depth look at the platform's capabilities.
The introduction of the Smart OS is a significant development for the cannabis extraction industry, offering operators tools to move from reactive guesswork to proactive control grounded in validated science and real-time insight. This innovation underscores the growing role of AI and IoT in optimizing industrial processes, with implications for cost savings, efficiency gains, and revenue growth in a competitive market.
