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 60,000 smart grid records with Terno AI, XGBoost hits 98% accuracy and a 0.998 ROC-AUC in classifying grid stability — with price elasticity and reaction-time features doing the heavy lifting.
This study shows that behavioral data alone can't reliably predict gaming toxicity — but a risk-based model combining behavioral and engineered features does a much better job of flagging the small segment of high-risk users driving disproportionate harm.
Using Terno AI's prompt-driven pipeline, an XGBoost model predicts Porter delivery times with 2.37-minute RMSE and 93.6% explained variance — turning driving duration and order congestion into a real-time ETA engine.
A machine learning framework using Terno AI benchmarks Logistic Regression, Random Forest, and XGBoost to predict clinical trial patient dropout — and shows why standardized preprocessing, not just model choice, is the key to real-world generalizability.
Scheduling Inefficiency vs. Logistics Route Failures: A Data-Driven Investigation
Analyzing 100,000 CMS collision events shows XGBoost predicts dielectron invariant mass with 0.98 R² by learning the non-linear physics that transverse momenta and electron energies encode, far outperforming linear regression's 0.41.
Comparing three text-representation techniques on 40,000 labeled Amazon reviews shows Word2Vec + SVM beats traditional BoW and TF-IDF models, hitting 91.4% accuracy by capturing semantic meaning that word-frequency methods miss.
Analyzing bank customer records reveals that age and account inactivity—not product breadth or rates—are the strongest predictors of churn, with machine learning models hitting 86.65% accuracy in flagging at-risk customers before they leave.
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.
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