Exploring Real-World AI Use Cases

Advance State-of-the-Art AI Applications in Your Field. Discover Practical, Battle-Tested Use Cases that Drive Real Business Impact.


Demand and production forecasting for renewables (solar and wind power plants) is extremely crucial for energy companies. Tracking weather conditions and other external data is one of the most important variables in the energy sector. Increase the accuracy of your forecasts with a wide range of data sources and our in-house technology—and start delivering custom-built solutions today.

Use Cases in Energy

Electricity Demand Forecasting

Unlike other energy sources, electricity cannot be saved or used later. Thus, there needs to be consistent predictions for daily usage. Otherwise, electricity distribution companies could incur massive hits to their bottom line and reputation..

Renewable Energy Sources Production Forecasting

Energy suppliers are obligated to share their forecasts with the system operator to maintain system balance. Renewable energy production is especially hard to predict because it depends on uncontrollable factors such as weather conditions.


FinTechs are implementing machine learning to cut down on fraud, reduce spending, and enhance operational efficiency. From fraud detection and credit risk forecasting to churn risk and targeted marketing campaigns, FinTechs are leveraging data-driven decision-making for enhanced profitability and to gain an advantage in a fiercely competitive market.

Use Cases in FinTech

Card Fraud Detection and Prevention

By analyzing vast amounts of data, FinTechs can use machine learning algorithms to identify patterns and anomalies—empowering proactive AI-powered fraud detection. AutoML helps FinTechs protect customers, reduce financial losses, and maintain the integrity of their platforms—and the security of their customers.

Targeted Marketing Wealth Management Campaigns

FinTechs specializing in wealth management want to augment their targeted marketing to attract and retain high-net-worth clients. By leveraging AutoML, FinTechs can analyze customer data—including risk tolerance, demographics, investment preferences, and financial goals. The more FinTechs can tailor personalized marketing campaigns—the better the conversion rates and long-term relationships with clients.


Insurance policies and claims have multiple operational, legal, and economical aspects for both the insurers and clients. There are remarkable solutions to protect every stakeholder from overpricing, fraud, and the one thing you can never get back—lost time.

Use Cases in Insurance

Claims Cost Optimization

Insurers want to hit the maximum cost optimization in claims—without reducing their standards of quality. Collecting the most up-to-date data for an insurance policy and benchmarking similar cases is an outstanding way to meet optimization benchmarks.

Fraud Detection

People can claim the cause of loss that is not covered by their insurance or try to obtain some advantage they are not entitled to with the fraudulent actions.

Automated Underwriting

In automated underwriting, the risk perspective of the system and the classification models of the client profiles are so crucial to determine the right assessment and approval path of the related issue. If the model construction of the system doesn’t cover all the issues, there will always be misevaluated cases. Keeping the model always up-to-date is another challenge.

Policy Pricing

The primary concern of pricing the policies is that one approach does not fit them all with traditional methods while calculating the payout price. Meaning that people with different backgrounds and incomes should be evaluated with different proposed prices.


Independent of the volume, most of the companies have been witnessing the complexity of stocks, sales predictions and pricing management while the business is expanding. There are smart ways for the management of all the variables with our solutions.

Use Cases in Retail

Sales Prediction

Forecasting sales is essential for retail companies since it directly affects identifying benchmarks and determining incremental impacts of new initiatives, planning resources in response to expected demand and projecting future budgets.

Price Optimization

The traditional market research into sales and customer satisfaction done by companies to optimize their prices can be very slow and may not serve companies’ best interests profit-wise in the long term.


The quality of production process and equipment’s robustness in every stage has a direct impact on the final product. Artificial Intelligence is helping us to evaluate the data always to keep the production performance on top.

Use Cases in Manufacturing

Quality Control

Mass production has an issue with a poor quality because of the human-driven quality control processes. In addition, there is a need to control the quality of products without interrupting (or disrupting) the production process.

Predictive Maintenance

The predictive maintenance is related to the equipment’s condition rather than expected life statistics. For this prediction, constant monitoring of the equipment, tracking of anomalies and other equipment operation-related data collection should be carried out to predict the maintenance periods to avoid equipment failure.

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