In the medical field, after the application of Notes AI individualized diagnosis and treatment system in Johns Hopkins Hospital, the system offered personalized treatment recommendations to 2,500 chronic disease patients on the basis of 368 biometric data (e.g., heart rate variability ±12bpm, difference of gene expression profile 0.37, etc.), and drug compliance was increased by 41%. Rate of hospitalization recurrence decreased by 28%. The quantitative data of Khan Academy, an ed-tech company, demonstrates that among the students using the integration of the Notes AI adaptive learning module, the system adaptively adjusts the knowledge push approach on the basis of 12 million hours of eye movement tracking (sampling rate 240Hz) and cognitive load index (median decreased from 78 to 23). The rate of retention of STEM knowledge was increased to 3.2 times over traditional teaching, and the difference in learning efficiency was statistically significant (p<0.001).
In manufacturing, Tesla Berlin factory’s Notes AI system produces AR visual instruction for engineers of different skill levels by processing multimodal data stream of 1.8TB/day in real time (torque deviation ±3.2N·m, welding temperature fluctuation ±15℃). New equipment personnel’s debugging efficiency reached 92% of the senior staff levels within 3 weeks, and 4.3 million US dollars in annual training expenses were saved. In the field of law, Linklinkers’ smart contract review module is dynamically adjusting the interface layout and information density according to 1,743 behavioral factors of lawyers (such as clause annotation speed, risk concern weight, etc.), which enhances the accuracy rate of identifying key clauses for new lawyers from 68% to 96%, and the difference coefficient of work efficiency drops from 0.38 to 0.11.
When Notes AI was plugged into the supply chain system of the giant retail firm Walmart, based on the analysis of 150 million consumer shopping patterns data (like 32% shopping cart abandonment rate, median shelf stay time of 9.7 seconds), differentiated replenishment models for different regions’ stores were generated, and inventory turnover was increased to 2.3 times above the industry average. The proportion of unsalable goods fell from 12.7% to 3.1%. According to IDC’s 2025 Intelligent Systems report, Notes AI’s user profile engine delivers 17 facets of real-time feature extraction (e.g., operation frequency 4.2 times/second, focus shift cycle 9.3 seconds), and its reinforcement learning algorithm performs 120 million rounds of strategy optimization in 24 hours. Cross-industry user adaptation efficiency is 7.8 times the conventional system.
Neuroscience tests have validated that Notes AI’s interactive multimodal system has the ability to dynamically adapt information presentation based on the user’s prefrontal cortex activation level (fMRI reading ±0.23T), decreasing cognitive resource usage by 63%. In the banking sector, after Goldman Sachs quantitative group used Notes AI’s tailored analysis module, 87% of analysts gained knowledge of expert-level derivative pricing model construction techniques within 3 hours, and the system adapted the interface based on observing 427 operational characteristics parameters (e.g., pivot table usage frequency 2.3 times/minute, and median depth of formula nesting 5 layers). Model construction error rate was reduced from 7.4% to 0.9%. Gartner predicts that by 2028, Notes AI will have 92% of enterprise personalization use cases addressed, and its hyperparameter optimization (learning rate 0.003, batch size 512) adaptive engine is pushing the intelligent edges of human-machine collaboration.