Use Cases

High-Performance Computing applied to Artificial Intelligence has a wide range of applications across many fields. Take a look at just some of the ways Aeternal Mentis’ technology can put AI to work for your company.

Asset Management

Investment Strategy: Artificial Intelligence can optimize the search for interconnected elements, such as the destination of funds, risk, and reward, in order to increase yield more effectively than rule-based systems.

Portfolio Building: AI can help improve and automate the construction of investment portfolios for asset management companies. Tools like “robo-advisors” are able to analyze a client’s goals and, based on management’s criteria, develop a personalized investment portfolio autonomously or semi-autonomously, at a lower cost.

Risk Management: Large datasets can be processed and analyzed in more detail, thanks to the incorporation and processing of Natural Language. The technology can be used to analyze companies, identify patterns in data with greater granularity and confidence, and identify and quantify risks more efficiently.

Customer Service: AI can increase customer service efficiency through the use of chatbot interfaces both within and outside of asset management companies. Customer service channels allow managers to build a relationship with their clients and help them understand their portfolio’s performance, in a shorter span of time. Fewer account managers can therefore provide better service to a larger number of clients.


Diagnosis: Deep Learning systems can replace complex sets of statistical rules designed specifically for each diagnosis, with the goal of correctly identifying correlations in the data in an automated, scalable way. While human radiologists receive extensive training in order to be able identify anomalies in MRIs, AI models offer the same human precision, in less time.

Medicine Development: The application of Artificial Intelligence to medicine development is allowing the pharmaceutical industry to shorten the amount of time it takes to bring a new drug to market, while reducing uncertainties about the development process. AI is capable of synthesizing and analyzing scientific research articles to reach hypotheses, predicting the behavior of compounds from the very early stages of the development process, and improving patient identification for clinical analyses.

Patient Monitoring: Monitoring the vital signs of hospitalized patients and at-risk patients that are at home is still a very labor-intensive, manual process. AI can synthesize the signals from each of the patients’ devices to offer clinical-grade supervision of a large group of patients, in real time, and with just one nurse. Plus, thanks to the predictive analysis of the data, it prevents hospital beds from being used unnecessarily.

Law & Compliance

Jurisprudence & Due Diligence: Natural Language Processing AI can identify, classify, and utilize content from unstructured databases and documents. The principal applications in this field are searching for and classifying jurisprudence and identifying key documents in the due diligence process. Artificial Intelligence has the potential to speed up operations and reduce cost.

Courtroom Strategy: Artificial Intelligence can analyze previous trials with more speed and detail than any other technology has been able to do in the past. It is able to anticipate the probability of other outcomes occurring, allowing lawyers to be better informed and improve their strategic decision-making for trial. In areas where there are a large number of cases, such as personal injury, computer programs can help a firm decide whether or not to accept a case. In areas that have high value, like business litigation, computer programs can suggest the probability of a certain outcome, based on the behavior of previous juries and the lawyers’ tendency to reach an agreement or go to court.

Compliance: Preventing infractions, from a confidential data breach to an email sent to the wrong address with a client database attached, is a challenge for rule-based systems. Upon getting to know users’ habits over time, Artificial Intelligence systems can raise the alarm about possible compliance infractions in real-time, or even before they happen.


Predictive Maintenance: The cost of downtime is high in every industry. Artificial Intelligence can identify specific patterns in the different sensors incorporated into the machinery used in the production process (such as vibration, temperature, pressure sensors, and more) to identify the main indicators of equipment failure. By more accurately predicting which components are more likely to fail and when, the pieces can be replaced proactively to prevent failure and avoid incurring the cost of inactivity.

Performance Improvement: For the most valuable assets, such as gas and wind turbines, Artificial Intelligence can be geared toward optimizing their performance in various ways. While rule-based systems offer limited results when they are applied to complex tasks, like the adjustment of combustion valves on a gas turbine, the application of neural networks can more effectively optimize consumption.

Utility Optimization: AI allows companies to anticipate and align their consumption of utilities like electricity and water, by adjusting in real time to match process requirements and therefore reducing consumption.

Consumer Goods

Customer Segmentation: Deep Learning’s Natural Language Processing abilities allow large retailers to increase datasets, including their potential clients’ social media profiles, with the objective of obtaining more precise segmentations. Additionally, Artificial Intelligence can also allow the companies to choose the most appropriate channels to use to market to their potential clients.

Content Personalization: The majority of the content that is shown to shoppers online is irrelevant or not suited to their preferences, which reduces the conversion rate. Just as with customer segmentation, AI offers additional unstructured datasets for a better multivariate analysis, with the goal of identifying a larger number of correlations than rule-based systems.

Price Optimization: AI is able to optimize prices in way that is more sophisticated than using traditional methods like “cost-plus,” “relative to competitors,” or “odd-even pricing” ($0.99). By identifying the correlations between datasets, Artificial Intelligence can better optimize the relevant factors, including price elasticity, revenue, profits, product availability, and the phases of the product lifecycle. It can even take willingness to pay into account.

Churn Prediction: Traditional programs waver when it comes time to incorporate new information sources, maximize the value of multivariate datasets, or offer detailed recommendations. When AI predicts customer churn, it can identify the main churn indicators more effectively and improve the adjustments made, through a more precise prediction of the format and content of the interventions, so that they will be successful.


Autonomous Vehicles: Artificial Intelligence computerized vision systems allow vehicles to perceive and identify physical characteristics and the dynamics of its surroundings, from lanes on the road to pedestrians and traffic lights, with a very high degree of accuracy. Combined with data processing algorithms and AI planning, cars, buses, and trucks can operate and orient themselves on their own, without human intervention.

Infrastructure and System Optimization: AI’s ability to detect patterns and optimize complex data can be applied to traffic issues, congestion, and general transportation system infrastructure. Predicting traffic patterns and modeling the deterioration of transportation infrastructure is possible, thanks to the application of Machine Learning and Deep Learning systems.

Fleet Management: Artificial Intelligence can optimize pickups, route planning, and delivery planning to maximize the use of assets, while also taking economic, social, and environmental impact into account. It can be applied to everything from logistics networks that sustain the economy to taxi fleets and last mile delivery.

Control Applications: Machine Learning systems adapt well to the numerous challenges in the fields of prediction and optimization, such as controlling air traffic, signaling vehicular traffic, and controlling railways.


Supply Management: AI models can be applied to numerous aspects of electricity supply management. These applications range from changes in the supply that derive from the intermittency of renewable energy production to the optimization of power grids, which are becoming more complex, as a consequence of the introduction of producer-consumers to the supply chain.

Demand Optimization: Since Artificial Intelligence is able to identify patterns in consumer behavior, the models are able to distribute energy from peak usage points and high prices to cheaper periods that have lower demand.

Security: Rule-based systems fight to provide security for the system, given the ever-changing nature of security threats. Upon identifying abnormal patterns in network behavior, Deep Learning can identify attacks on the network’s security systems that evade traditional programs.

Customer Experience: Chatbots offer consumers self-service account administration, as well as information about products and customer service applied to the energy industry.