Predictive Modeling – Use Cases
We built a cloud-native SaaS application capable of scaling seamlessly to 1M+ active users. The platform featured secure APIs, subscription billing, and real-time analytics.
We developed AI-driven forecasting models that analyzed sales history, promotions, and seasonality. Retailers reduced excess inventory by 35% and minimized stock-outs, ensuring the right products were always available at the right time. This directly improved customer satisfaction and boosted sales revenue.
Using machine learning models, we automated creditworthiness assessments by analyzing transaction patterns, income stability, and historical repayment behavior. This reduced loan defaults by 20–25% while enabling faster loan approvals, improving both profitability and customer experience.
We implemented predictive models that scanned claim histories, customer behavior, and external datasets to flag fraudulent claims. This helped insurers reduce fraudulent payouts by up to 30%, saving millions annually and improving operational efficiency.
By leveraging patient records and lab data, we built models that predicted readmissions and disease progression. Hospitals used these insights to proactively intervene, reducing readmission rates by 18% and improving long-term patient outcomes.
