H2O.ai

Ingest historical transaction data from Snowflake.

Use Driverless AI to engineer time-since-last-transaction features.

Train an ensemble of LightGBM and Random Forest models.

Deploy the model as a MOJO into a streaming Kafka pipeline.

Monitor for drift using H2O MLOps.

Upload multi-year sales data with seasonal triggers.

Apply the time-series specialized recipe in H2O AutoML.

Calculate lag variables and moving averages automatically.

Forecast next 30-day demand with confidence intervals.

Export forecasts to ERP via REST API.

Connect H2O Document AI to a document repository.

OCR and parse complex PDF tables into structured data.

Index documents into the h2oGPTe vector database.

Use natural language queries to ask compliance questions.

Review citations and source document snippets for verification.

Process claim text and attached damage photos using Hydrogen Torch.

Extract key entities from text and detect damage severity in images.

Combine image and text features in a multi-modal H2O model.

Route high-severity claims to senior adjusters automatically.

Track model decision logic via SHAP impact values.

Integrate customer CRM data and usage logs.

Run AutoML to identify the primary drivers of churn (e.g., support tickets).

Generate a risk score for every customer daily.

Trigger automated marketing offers for customers with scores > 0.8.

A/B test the model efficacy in H2O MLOps.