Artificial Intelligence has achieved prominence in recent years. But surprisingly, we are experiencing AI without understanding it completely. The most basic uses are Image tagging and ‘Spam’ detection. Now AI automatically tags uploaded images using face or image recognition technique, and the pattern or selected words to filter spam messages. Let’s look at some of the significant business problems solved by ONPASSIVE AI.
1. MANUAL DATA ENTRY
Duplication and inaccuracy of data are major business problems for a business desiring to automate its processes. ONPASSIVE AI algorithms and predictive modeling algorithms can significantly boost the situation. AI discovered data to enhance the process as further calculations are made. Thus systems can learn to execute data entry tasks and time-intensive documentation.
2. DETECTING SPAM
Spam detection is the earliest difficulty solved by AI. Now the spam filters generate new rules themselves using Machine Learning. Brain-like “neural networks” in the spam filters can study to identify junk mail and phishing messages by interpreting rules beyond an enormous collection of computers. In addition to spam detection, social media sites are using AI as a way to recognize and filter abuse.
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3. PRODUCT RECOMMENDATION
Unsupervised learning allows a product based recommendation system. Given a shopping history for a customer and a vast inventory of products, ONPASSIVE AI models can recognize those products in which that customer will be engaged and likely to complete the purchase. The algorithm recognizes the hidden patterns among items and focuses on segregating similar products into clusters.
5. CUSTOMER SEGMENTATION AND LIFETIME VALUE PREDICTION
Churn prediction, customer segmentation, and customer lifetime value prediction are the principal challenges confronted by any marketer. Businesses have a large amount of marketing relevant data from multiple sources such as website visitors, email campaigns, and lead data. Utilizing ONPASSIVE AI can accurately predict individual marketing offers and incentives achieved.
6. FINANCIAL ANALYSIS
Due to a large amount of data, accurate historical data, and quantitative nature, ONPASSIVE AI can be used in financial analysis. Current usage cases of ML in finance involve algorithmic trading, fraud detection, portfolio management, and loan underwriting. It enables constant evaluations of data for analysis and detection of nuances and anomalies to enhance the precision of rules and models.
7. PREDICTIVE MAINTENANCE
The manufacturing industry can utilize ONPASSIVE AI and ML to identify significant patterns in factory data. Preventive and corrective maintenance methods are inefficient and costly. Whereas predictive maintenance reduces the risk of unforeseen failures and decreases the amount of additional preventive maintenance exercises.
8. IMAGE RECOGNITION (COMPUTER VISION)
ONPASSIVE AI vision presents symbolic or numeric knowledge from high-dimensional data and images. It includes data mining, machine learning, pattern recognition, and database knowledge discovery. The potential businesses that utilize image recognition technology are automobiles, marketing campaigns, healthcare, etc. Image recognition based marketing campaigns drives user engagement and social sharing.
ONPASSIVE AI platform will no doubt speed up the analysis part, help businesses identify risks, and deliver more reliable service. But the quality of data is the primary blockage for many companies. Hence apart from knowledge of AI algorithms, businesses are required to structure the data before utilizing the data models.
