Make faster decisions with your data. Terno AI helps teams explore databases using natural language, delivering instant insights without waiting on analysts or writing SQL.
Analyzing 1.3M cosmetics reviews alongside global search trends shows aesthetic skincare interest is rising worldwide, but organic products still dominate—and machine learning clustering reveals consumer satisfaction depends on far more than trend exposure alone.
A spatiotemporal modeling approach to predict risk exposure and recommend safer routes, timings, and safety measures
Bitcoin swings wildly even within 15-minute windows — so a new study built regression models on 7+ years of high-frequency trading data to predict its next move. The twist: XGBoost looked flawless in training, then collapsed completely on real test data, while a tuned Random Forest quietly delivered the most reliable results.
Millions of children finish primary school unable to read. This case study shows how an AI-driven platform turns fragmented global education data into forecasts, risk flags, and policy simulations — revealing that Sub-Saharan Africa has both the worst learning poverty rates and the fastest improvement.
CNC (Computer Numerical Control) machining failures cost manufacturers real money. A new case study analyzes 10,000 machining records to pinpoint failure-predicting thresholds and build a Random Forest model with 98.4% accuracy.
Analyzing flight records reveals that online boarding and inflight entertainment — not seat comfort — are the strongest drivers of passenger satisfaction. See how Terno AI's 96.37%-accurate ML model helps airlines predict and prevent dissatisfaction before it happens.
A case study uses RFM analysis and K-Means clustering to segment e-commerce customers into loyal high-value, inactive, and nurturable groups — finding that purchase frequency, not order size, drives customer value most.
Air quality monitors tell you how polluted the air is — never why. A new study uses machine learning to classify pollution across ten Pakistani cities as Dust, Anthropogenic, or Mixed, using only standard pollutant data. The result: an XGBoost model hitting 98.6% accuracy, and a striking finding — nearly 60% of pollution is actually a blend of natural and human causes, not one dominant source.
A predictive modeling and data-driven approach to predict degrading mental health
This project develops an AI-powered trade intelligence system for India's import and export data from 2006–2025. The platform integrates macroeconomic indicators such as GDP, CPI, Brent crude oil prices, USD/INR exchange rates, tariffs, and international trade data from over 250 countries. Machine learning models forecast future imports, exports, trade deficits, commodity demand, and country-wise trade patterns. The system helps policymakers, businesses, investors, and researchers make data-driven decisions under changing global economic conditions.
Three models hit 97-99% accuracy predicting whether an AI agent's access request should be allowed, blocked, or sent to a human. Then a one-column baseline scored 84%, and a three-line if-statement scored 95%. This is what the audit found, why near-perfect accuracy on a security task should worry you, and how the same blind spot let a real zero-click attack through Microsoft 365 Copilot.
Can your keyboard and mouse reveal how stressed you are — without you saying a word? This case study explores stress detection using keystroke dynamics, mouse movement patterns, and application usage data, evaluated through an end-to-end machine learning pipeline.
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