Decision AI for Data Engineering with Jev

Data pipelines often hit a gap: some decisions are too complex for hand-written rules but don't need a full language model. Hardcoded logic breaks when data gets messy. LLMs are slow, expensive, and can hallucinate, making them risky inside fast-moving pipelines. Jev is a "system one" model built for exactly this space. It produces type-safe structured decisions with calibrated probability scores in 70 to 500ms, far faster and cheaper than LLMs, with no hallucinations by design.

In this code-along webinar, Deepak Goyal, Founder at Azurelib Academy, will show you how to use the Jev "system one" model to add intelligent decision nodes to real data pipelines. You will learn how Jev classifies, routes, and scores data with the speed and reliability that LLMs cannot match. Deepak will walk through building a working pipeline using Jev, Airflow, and Azure. By the end, you will be able to replace brittle rule-based logic with fast, reliable AI decisions in your own pipelines.