SOLUTIONS

Use cases that ship in days, not quarters

We've mapped the common ML problems in each industry to out-of-the-box Langsat workflows. Pick your use case, connect your data, get a model.

By industry

Click any vertical to see example problems, data shape, and a reference customer story.

Demand forecasting, personalization, stockout prevention, cart abandonment.

Fraud detection, credit scoring, transaction categorization, AML signals.

Readmission risk, patient cohort clustering, no-show prediction.

Predictive maintenance, quality control, supply-chain forecasting.

Churn prediction, PQL scoring, usage-based upgrade propensity.

BY PROBLEM TYPE

Most Langsat customers start with one of these four patterns — regardless of industry.

Churn, fraud, risk, categorization. Binary or multi-class targets on a row-per-entity table.

Demand, revenue, LTV, price sensitivity. Continuous targets with time-series depth.

Recommendations, network expansion, matching. Predict the existence of edges.

Fraud, churn early-warning, quality control. Unsupervised + supervised blends.