By combining brain-computer interfaces with semantic parsing techniques, Notes AI was able to convert human thoughts into actionable commands with 92 percent accuracy, and its EEG signal decoding system could recognize 87 types of user intent in 300 milliseconds. In 2025, a study published in Nature Neuroscience showed that the system accurately converted neural signals to movements of a robotic arm with an error of 0.3 mm in the motor imagination experiment in Parkinson’s disease patients, 63% better than traditional ways. For example, German NeuroTech medical group used Notes AI to double the success rate of amputees to grasp objects with mind-controlled prosthetics from 68% to 94%, and the reaction time was reduced to 500 milliseconds, close to the biological limit.
In creative realization, Notes AI’s generative model reduces the conceptual prototyping process by 80%. According to the data of an industrial design company, when the designer enters the keyword “foldable new energy vehicle”, the system is able to generate a 3D model within 12 minutes, and reduce the body weight by 18% through the topology optimization algorithm, and the torsional stiffness is increased to 27,500Nm/deg. The 2026 Tesla manufacturing log states that with the use of the Notes AI thought-driven design platform, the development cycle for the Model Z prototype was reduced from 22 months to 7 months, the cost of manufacture was reduced by $23,000, and the aerodynamic efficiency (Cd value) was maximized to 0.19, an industry record.
For rolling out complex decisions, Notes AI’s cognitive modeling technology boasts a 97% mapping rate from the strategic blueprint to the KPI system. Morgan Stanley 2027 report said that when the leaders put forward the idea, “Asia Pacific market expansion” in a meeting, the system read the feasibility data of 37 factors in real time (e.g., the standard deviation of geopolitical risk index volatility 2.7, the median value of supply chain resilience score 84.5), and generated an implementation plan with 127 action items within 45 seconds. 4 times faster decision-making execution. Following a multinational retail group’s application, the new market entry cycle reduced from 18 months to 5.2 months, while the ROI reached 37% in the initial year, a 15 percentage point improvement on the industry standard.
In the area of cross-modal production, Notes AI’s multi-sensor fusion system facilitates closed-loop control from the brain to the world. After the Boston Dynamics Atlas robot is equipped with this technology, the operator only needs to think “moving a 20kg container to the B2 zone”, and the robot can plan its own path (the success rate of obstacle avoidance is 99.2%), adjust the grasping force (the pressure sensor error is ±0.05N), and monitor the tilt Angle of the goods in real time (the threshold is <3°). According to the data measured by Shanghai Port in 2028, such thinking-based automation has improved efficiency of container handling by 58%, reduced labor cost by 43%, and reduced the rate of accidents from 0.7 times per million boxes to 0.09 times.
These breakthroughs demonstrate the industrialization potential of neurointelligent technologies – Gartner estimates that by 2030, mind-controlled systems will control 39% of manufacturing situations. As the Notes AI Chief scientist stated during the NeurIPS 2029 keynote: “When we use alpha wave signals to directly control nanoscale 3D printers, humanity is crossing the final 1-nanometer gap from cognition to creation.”
