In the medical training field, Mayo Clinic’s resident training program employed Notes AI to enhance the median clinical knowledge test score by 29.7%, while creating the diagnostic chain of logic in complex cases using intelligent graph technology was 4.3 times more effective. The system’s reinforcement learning model, built on 2.5 million digital medical records, modifies the approach of dynamically pushing knowledge along the path of learner’s cognition (78 points of behavior data per second) and speeds up the long-term memory retention rate of noteworthy points of diagnosis and treatment from 41% to 83% within the traditional training. The achievement was named as among the 2023 medical education innovations by Lancet Digital Health issue. Pharmaceutical giant Novartis used Notes AI’s R&D knowledge management system to increase data association efficiency across clinical trials to 1,200 unstructured records per second, increase knowledge reuse rate of new drug application materials from 38% to 91%, and increase IND (New drug clinical trial) application approval rate by 27 percentage points.
In education technology, after Khan Academy integrated Notes AI’s personalized learning system, the slope of students’ knowledge retention curve in STEM topics increased by 2.8 times. Employing eye tracking (240Hz sampling rate) and brain wave inspection (monitoring θ wave to gamma wave ratio), the cognitive load model of the system improved knowledge absorption efficiency by 63%. On the controlled trial of 12,000 students, the retention rate for knowledge for the Notes AI group was 79% after 6 months, 41 percentage points above that of the control. In the legal industry, Linklaters’ knowledge management system increases the precision of retrieval of previous cases to 98.4% through the semantic retrieval module of Notes AI, reduces the retrieval time of similar legal clauses from 4.7 minutes per case to 0.9 seconds, and improves the efficiency of novice lawyers’ learning cases by 3.6 times with the intelligent summarizing feature.
In the manufacturing field, Toyota Motor utilizes the fault diagnosis skills of Notes AI to shorten the skill transfer loop of manufacturing line engineers from 18 months in the previous mentoring system to 5.2 months. The technology uses AR glasses to analyze 45,000 sensor signals every second of real-time equipment status data and 3D visual knowledge maps to improve the accuracy of advanced mechanical failures to 96.7% while reducing annual downtime cost of $230 million. In the oil and gas sector, Saudi Aramco has reduced the training cycle of rock formation identification skill for drilling engineers to 11 months from 3.2 years using the Notes AI geological expertise management system. The deep learning model of the system, based on 1.4PB historical data, can simultaneously compare 32 geological parameters in real time, enhancing the knowledge accuracy of exploration decisions to 94.5%.
According to the enterprise knowledge management, integration of Notes AI on Microsoft Teams platform has improved the cross-departmental knowledge retrieval speed to 5.8 times compared with traditional process. With regard to the 28 billion parameter multi-modal model, the smart integration among conference recording, document chart and instant message is realized, and the accuracy of knowledge reusability reaches 92.3%. According to Deloitte Consulting’s case study, consultants’ project start-up teams leveraging Notes AI were 4.1 times more effective, increased the integrity of major industry insight extraction from 68 percent to 97 percent, and positively affected client delivery cycles by 29 percent directly. Neuroscience experiments have confirmed that Notes AI’s spaced repetition algorithm (optimized based on the Ebbinghaus forgetting curve) improves knowledge consolidation to 3.7 times the effectiveness of traditional memory methods, and in 1,500 participants, the system group retained an information retention rate of 88.5±3.2% at 30 days. Significantly higher than control group 51.7±6.8% (p<0.001).
Gartner’s 2025 Knowledge Management report points out that companies using Notes AI reduce the company’s institutional knowledge loss rate to 28% of the industry average, and its underlying technology architecture includes: 1) A 45 billion parameter multimodal cognitive engine with support for 19 knowledge intelligent coding; 2) The distributed memory network enables 5.3TB of unstructured data processing per second; 3) Support for 107 domains of expertise through dynamic knowledge maps. The application case of Walmart’s supply chain department proves that Notes AI has increased the transmission efficiency of operation experience by 6.2 times, the decision accuracy of new employees has reached 92% of senior employees in 3 months, the technology has been examined by the IEEE Standards Committee, and the Knowledge transfer Efficiency index (KTEI) reached 9.7/10, exceeding industry standards. These statistics attest that Notes AI is remapping the boundaries of cognitive performance in the digital age by redesigning the whole chain of “information acquisition – cognitive reinforcement – knowledge consolidation” and is expected to account for 61.3% of the global enterprise knowledge management market by 2028 (a CAGR of 67.8%).