PostgresML

Ingest transaction data into Postgres.

Train a classification model on historical transaction data.

Use the model to predict the likelihood of fraud for new transactions.

Flag suspicious transactions for review.

Collect user interaction data (e.g., clicks, purchases).

Create user embeddings based on their behavior.

Use vector search to find similar users.

Recommend products that similar users have liked.

Collect sensor data from equipment.

Train a regression model to predict time-to-failure.

Alert maintenance teams when equipment is likely to fail.

Schedule proactive maintenance.

Collect customer data (e.g., demographics, usage).

Train a classification model to predict churn probability.

Identify high-risk customers.

Implement retention strategies.

Collect customer reviews and feedback.

Use a pre-trained LLM to analyze sentiment.

Categorize feedback as positive, negative, or neutral.

Identify areas for improvement.

Load the document into Postgres.

Chunk the document into smaller segments.

Use a pre-trained LLM to generate a summary.

Present the summary to the user.