How to use Jev to automate your business (Step-by-step w/ Treg)
This talk introduces Jev, a specialized model designed for reliable, high-accuracy business automation rather than creative text generation. Unlike general-purpose LLMs, Jev is optimized for structured decision-making, providing probability distributions for a limited set of options. This makes it ideal for mission-critical workflows requiring near-100% accuracy, such as fraud detection, internal link mapping, and classifying user intent, while being significantly faster and cheaper than large general models.
Key takeaways
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Jev's Core Advantage
Jev is designed for reliable, high-quality decision-making, outputting the probability of a list of given answers rather than predicting text token by token. This makes it extremely fast and cost-effective for high-volume business workflows.
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Confidence Scoring
2:00
Every answer Jev provides comes with a probability distribution (confidence score). This allows developers to build sophisticated business logic (e.g., if confidence > 70%, auto-block; if 35% < confidence < 70%, request human review).
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Use Case: Browser Automation
3:40
Jev can predict the next action (click, type) and the target UI element based on the DOM and interaction history, enabling fast and accurate browser and computer use for agent systems.
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Workflow Example: Fraud Detection
7:30
By combining Jev with data services like Track, users can build automated pipelines to classify signups (e.g., fraud, upsell value, affiliate) using thousands of data points, making previously uneconomical automation possible.