Confluent Pitch

How does Confluent fit with AI/ML pipelines?

AI models are only as strong as the freshness and quality of their data. Most organizations still rely on batch pipelines, which can make features stale by the time they reach the model.

Confluent enables real-time feature streaming into AI/ML systems. For example:

• Streaming transactional data for fraud detection in milliseconds.
• Feeding user activity streams into recommendation models for personalization.
• Serving IoT data to predictive maintenance models without delay.

Confluent integrates with feature stores, data lakes, and warehouses, but it acts as the real-time backbone that ensures models are operating on the most current data.

What do you think about Confluent’s “data in motion” vision?

Traditionally, data has been treated as something you collect, store, and query later — that’s data at rest. Confluent’s vision of ‘data in motion’ shifts this paradigm: data is continuously flowing and can be acted upon immediately.

This approach is becoming critical because businesses don’t just want to know what happened — they want to react as it happens. Whether it’s personalizing an app experience in real time or flagging fraudulent transactions instantly, data in motion is what makes those possible.

Confluent’s mission is to make streaming as easy and ubiquitous as traditional databases, and that’s where its long-term impact lies.

Give me an example of a real-world problem where Confluent delivers clear business value

A classic example is fraud detection in financial services. With a batch system, suspicious activity might be flagged hours later, after the fraud has already occurred.

With Confluent, every transaction stream can be analyzed in real time. Rules or ML models can immediately detect anomalies — like an unusual login location or abnormal transfer pattern — and trigger an action before money is lost.

The business value is obvious: reduced fraud losses, better compliance, and improved customer trust.