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.
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.
The Enterprise Reality: Why Web-Based AI Falls Short Enterprise environments operate under strict security and infrastructure constraints.
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